From 58daab21cd043e1dc37024a7f99b396788372918 Mon Sep 17 00:00:00 2001
From: Daniel Baumann <daniel.baumann@progress-linux.org>
Date: Sat, 9 Mar 2024 14:19:48 +0100
Subject: Merging upstream version 1.44.3.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
---
 web/server/h2o/libh2o/deps/brotli/enc/bit_cost.h | 139 +++++++++++++++++++++++
 1 file changed, 139 insertions(+)
 create mode 100644 web/server/h2o/libh2o/deps/brotli/enc/bit_cost.h

(limited to 'web/server/h2o/libh2o/deps/brotli/enc/bit_cost.h')

diff --git a/web/server/h2o/libh2o/deps/brotli/enc/bit_cost.h b/web/server/h2o/libh2o/deps/brotli/enc/bit_cost.h
new file mode 100644
index 000000000..32ad52e89
--- /dev/null
+++ b/web/server/h2o/libh2o/deps/brotli/enc/bit_cost.h
@@ -0,0 +1,139 @@
+/* Copyright 2013 Google Inc. All Rights Reserved.
+
+   Distributed under MIT license.
+   See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
+*/
+
+// Functions to estimate the bit cost of Huffman trees.
+
+#ifndef BROTLI_ENC_BIT_COST_H_
+#define BROTLI_ENC_BIT_COST_H_
+
+
+
+#include "./entropy_encode.h"
+#include "./fast_log.h"
+#include "./types.h"
+
+namespace brotli {
+
+static inline double ShannonEntropy(const uint32_t *population, size_t size,
+                                    size_t *total) {
+  size_t sum = 0;
+  double retval = 0;
+  const uint32_t *population_end = population + size;
+  size_t p;
+  if (size & 1) {
+    goto odd_number_of_elements_left;
+  }
+  while (population < population_end) {
+    p = *population++;
+    sum += p;
+    retval -= static_cast<double>(p) * FastLog2(p);
+ odd_number_of_elements_left:
+    p = *population++;
+    sum += p;
+    retval -= static_cast<double>(p) * FastLog2(p);
+  }
+  if (sum) retval += static_cast<double>(sum) * FastLog2(sum);
+  *total = sum;
+  return retval;
+}
+
+static inline double BitsEntropy(const uint32_t *population, size_t size) {
+  size_t sum;
+  double retval = ShannonEntropy(population, size, &sum);
+  if (retval < sum) {
+    // At least one bit per literal is needed.
+    retval = static_cast<double>(sum);
+  }
+  return retval;
+}
+
+
+template<int kSize>
+double PopulationCost(const Histogram<kSize>& histogram) {
+  if (histogram.total_count_ == 0) {
+    return 12;
+  }
+  int count = 0;
+  for (int i = 0; i < kSize; ++i) {
+    if (histogram.data_[i] > 0) {
+      ++count;
+    }
+  }
+  if (count == 1) {
+    return 12;
+  }
+  if (count == 2) {
+    return static_cast<double>(20 + histogram.total_count_);
+  }
+  double bits = 0;
+  uint8_t depth_array[kSize] = { 0 };
+  if (count <= 4) {
+    // For very low symbol count we build the Huffman tree.
+    CreateHuffmanTree(&histogram.data_[0], kSize, 15, depth_array);
+    for (int i = 0; i < kSize; ++i) {
+      bits += histogram.data_[i] * depth_array[i];
+    }
+    return count == 3 ? bits + 28 : bits + 37;
+  }
+
+  // In this loop we compute the entropy of the histogram and simultaneously
+  // build a simplified histogram of the code length codes where we use the
+  // zero repeat code 17, but we don't use the non-zero repeat code 16.
+  size_t max_depth = 1;
+  uint32_t depth_histo[kCodeLengthCodes] = { 0 };
+  const double log2total = FastLog2(histogram.total_count_);
+  for (size_t i = 0; i < kSize;) {
+    if (histogram.data_[i] > 0) {
+      // Compute -log2(P(symbol)) = -log2(count(symbol)/total_count) =
+      //                          =  log2(total_count) - log2(count(symbol))
+      double log2p = log2total - FastLog2(histogram.data_[i]);
+      // Approximate the bit depth by round(-log2(P(symbol)))
+      size_t depth = static_cast<size_t>(log2p + 0.5);
+      bits += histogram.data_[i] * log2p;
+      if (depth > 15) {
+        depth = 15;
+      }
+      if (depth > max_depth) {
+        max_depth = depth;
+      }
+      ++depth_histo[depth];
+      ++i;
+    } else {
+      // Compute the run length of zeros and add the appropriate number of 0 and
+      // 17 code length codes to the code length code histogram.
+      uint32_t reps = 1;
+      for (size_t k = i + 1; k < kSize && histogram.data_[k] == 0; ++k) {
+        ++reps;
+      }
+      i += reps;
+      if (i == kSize) {
+        // Don't add any cost for the last zero run, since these are encoded
+        // only implicitly.
+        break;
+      }
+      if (reps < 3) {
+        depth_histo[0] += reps;
+      } else {
+        reps -= 2;
+        while (reps > 0) {
+          ++depth_histo[17];
+          // Add the 3 extra bits for the 17 code length code.
+          bits += 3;
+          reps >>= 3;
+        }
+      }
+    }
+  }
+  // Add the estimated encoding cost of the code length code histogram.
+  bits += static_cast<double>(18 + 2 * max_depth);
+  // Add the entropy of the code length code histogram.
+  bits += BitsEntropy(depth_histo, kCodeLengthCodes);
+  return bits;
+}
+
+}  // namespace brotli
+
+#endif  // BROTLI_ENC_BIT_COST_H_
-- 
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