Update brotli to v1.0.0-snapshot.

third_party/brotli/enc/bit_cost_inc.h

+/* NOLINT(build/header_guard) */
 /* 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.
+/* template parameters: FN */
 
-#ifndef BROTLI_ENC_BIT_COST_H_
-#define BROTLI_ENC_BIT_COST_H_
+#define HistogramType FN(Histogram)
 
-#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) {
+double FN(BrotliPopulationCost)(const HistogramType* histogram) {
   static const double kOneSymbolHistogramCost = 12;
   static const double kTwoSymbolHistogramCost = 20;
   static const double kThreeSymbolHistogramCost = 28;
   static const double kFourSymbolHistogramCost = 37;
-  if (histogram.total_count_ == 0) {
+  const size_t data_size = FN(HistogramDataSize)();
+  int count = 0;
+  size_t s[5];
+  double bits = 0.0;
+  size_t i;
+  if (histogram->total_count_ == 0) {
     return kOneSymbolHistogramCost;
   }
-  int count = 0;
-  int s[5];
-  for (int i = 0; i < kSize; ++i) {
-    if (histogram.data_[i] > 0) {
+  for (i = 0; i < data_size; ++i) {
+    if (histogram->data_[i] > 0) {
       s[count] = i;
       ++count;
       if (count > 4) break;
     }
   }
   if (count == 1) {
     return kOneSymbolHistogramCost;
   }
   if (count == 2) {
-    return (kTwoSymbolHistogramCost +
-            static_cast<double>(histogram.total_count_));
+    return (kTwoSymbolHistogramCost + (double)histogram->total_count_);
   }
   if (count == 3) {
-    const uint32_t histo0 = histogram.data_[s[0]];
-    const uint32_t histo1 = histogram.data_[s[1]];
-    const uint32_t histo2 = histogram.data_[s[2]];
-    const uint32_t histomax = std::max(histo0, std::max(histo1, histo2));
+    const uint32_t histo0 = histogram->data_[s[0]];
+    const uint32_t histo1 = histogram->data_[s[1]];
+    const uint32_t histo2 = histogram->data_[s[2]];
+    const uint32_t histomax =
+        BROTLI_MAX(uint32_t, histo0, BROTLI_MAX(uint32_t, histo1, histo2));
     return (kThreeSymbolHistogramCost +
             2 * (histo0 + histo1 + histo2) - histomax);
   }
   if (count == 4) {
     uint32_t histo[4];
-    for (int i = 0; i < 4; ++i) {
-      histo[i] = histogram.data_[s[i]];
+    uint32_t h23;
+    uint32_t histomax;
+    for (i = 0; i < 4; ++i) {
+      histo[i] = histogram->data_[s[i]];
     }
-    // Sort
-    for (int i = 0; i < 4; ++i) {
-      for (int j = i + 1; j < 4; ++j) {
+    /* Sort */
+    for (i = 0; i < 4; ++i) {
+      size_t j;
+      for (j = i + 1; j < 4; ++j) {
         if (histo[j] > histo[i]) {
-          std::swap(histo[j], histo[i]);
+          BROTLI_SWAP(uint32_t, histo, j, i);
         }
       }
     }
-    const uint32_t h23 = histo[2] + histo[3];
-    const uint32_t histomax = std::max(h23, histo[0]);
+    h23 = histo[2] + histo[3];
+    histomax = BROTLI_MAX(uint32_t, h23, histo[0]);
     return (kFourSymbolHistogramCost +
             3 * h23 + 2 * (histo[0] + histo[1]) - histomax);
   }
 
-  // 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.
-  double bits = 0;
-  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;
+  {
+    /* 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[BROTLI_CODE_LENGTH_CODES] = { 0 };
+    const double log2total = FastLog2(histogram->total_count_);
+    for (i = 0; i < data_size;) {
+      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 = (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 {
-        reps -= 2;
-        while (reps > 0) {
-          ++depth_histo[17];
-          // Add the 3 extra bits for the 17 code length code.
-          bits += 3;
-          reps >>= 3;
+        /* 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;
+        size_t k;
+        for (k = i + 1; k < data_size && histogram->data_[k] == 0; ++k) {
+          ++reps;
+        }
+        i += reps;
+        if (i == data_size) {
+          /* 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[BROTLI_REPEAT_ZERO_CODE_LENGTH];
+            /* 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 += (double)(18 + 2 * max_depth);
+    /* Add the entropy of the code length code histogram. */
+    bits += BitsEntropy(depth_histo, BROTLI_CODE_LENGTH_CODES);
   }
-  // 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_
+#undef HistogramType