Update brotli to v1.0.0-snapshot.

third_party/brotli/enc/bit_cost.h

Index: third_party/brotli/enc/bit_cost.h
diff --git a/third_party/brotli/enc/bit_cost.h b/third_party/brotli/enc/bit_cost.h
index 4652006864c581c6589b92e50932251fca5a9124..e69ee04a1876ea69ebb082795ec0b6ff19b7b6d9 100644
--- a/third_party/brotli/enc/bit_cost.h
+++ b/third_party/brotli/enc/bit_cost.h
@@ -4,19 +4,22 @@
    See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
 */
 
-// Functions to estimate the bit cost of Huffman trees.
+/* 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 <brotli/types.h>
 #include "./fast_log.h"
-#include "./types.h"
+#include "./histogram.h"
+#include "./port.h"
 
-namespace brotli {
+#if defined(__cplusplus) || defined(c_plusplus)
+extern "C" {
+#endif
 
-static inline double ShannonEntropy(const uint32_t *population, size_t size,
-                                    size_t *total) {
+static BROTLI_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;
@@ -27,135 +30,34 @@ static inline double ShannonEntropy(const uint32_t *population, size_t size,
   while (population < population_end) {
     p = *population++;
     sum += p;
-    retval -= static_cast<double>(p) * FastLog2(p);
+    retval -= (double)p * FastLog2(p);
  odd_number_of_elements_left:
     p = *population++;
     sum += p;
-    retval -= static_cast<double>(p) * FastLog2(p);
+    retval -= (double)p * FastLog2(p);
   }
-  if (sum) retval += static_cast<double>(sum) * FastLog2(sum);
+  if (sum) retval += (double)sum * FastLog2(sum);
   *total = sum;
   return retval;
 }
 
-static inline double BitsEntropy(const uint32_t *population, size_t size) {
+static BROTLI_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);
+    /* At least one bit per literal is needed. */
+    retval = (double)sum;
   }
   return retval;
 }
 
-template<int kSize>
-double PopulationCost(const Histogram<kSize>& 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) {
-    return kOneSymbolHistogramCost;
-  }
-  int count = 0;
-  int s[5];
-  for (int i = 0; i < kSize; ++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_));
-  }
-  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));
-    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]];
-    }
-    // Sort
-    for (int i = 0; i < 4; ++i) {
-      for (int j = i + 1; j < 4; ++j) {
-        if (histo[j] > histo[i]) {
-          std::swap(histo[j], histo[i]);
-        }
-      }
-    }
-    const uint32_t h23 = histo[2] + histo[3];
-    const uint32_t histomax = std::max(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;
-      } 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;
-}
+BROTLI_INTERNAL double BrotliPopulationCostLiteral(const HistogramLiteral*);
+BROTLI_INTERNAL double BrotliPopulationCostCommand(const HistogramCommand*);
+BROTLI_INTERNAL double BrotliPopulationCostDistance(const HistogramDistance*);
 
-}  // namespace brotli
+#if defined(__cplusplus) || defined(c_plusplus)
+}  /* extern "C" */
+#endif
 
-#endif  // BROTLI_ENC_BIT_COST_H_
+#endif  /* BROTLI_ENC_BIT_COST_H_ */