mirror of
https://github.com/xiaoyifang/goldendict-ng.git
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453 lines
25 KiB
C
453 lines
25 KiB
C
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/*
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* Copyright (c) Yann Collet, Facebook, Inc.
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* All rights reserved.
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*
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* This source code is licensed under both the BSD-style license (found in the
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* LICENSE file in the root directory of this source tree) and the GPLv2 (found
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* in the COPYING file in the root directory of this source tree).
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* You may select, at your option, one of the above-listed licenses.
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*/
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#ifndef DICTBUILDER_H_001
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#define DICTBUILDER_H_001
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#if defined (__cplusplus)
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extern "C" {
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#endif
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/*====== Dependencies ======*/
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#include <stddef.h> /* size_t */
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/* ===== ZDICTLIB_API : control library symbols visibility ===== */
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#ifndef ZDICTLIB_VISIBILITY
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# if defined(__GNUC__) && (__GNUC__ >= 4)
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# define ZDICTLIB_VISIBILITY __attribute__ ((visibility ("default")))
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# else
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# define ZDICTLIB_VISIBILITY
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# endif
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#endif
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#if defined(ZSTD_DLL_EXPORT) && (ZSTD_DLL_EXPORT==1)
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# define ZDICTLIB_API __declspec(dllexport) ZDICTLIB_VISIBILITY
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#elif defined(ZSTD_DLL_IMPORT) && (ZSTD_DLL_IMPORT==1)
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# define ZDICTLIB_API __declspec(dllimport) ZDICTLIB_VISIBILITY /* It isn't required but allows to generate better code, saving a function pointer load from the IAT and an indirect jump.*/
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#else
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# define ZDICTLIB_API ZDICTLIB_VISIBILITY
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#endif
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/*******************************************************************************
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* Zstd dictionary builder
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*
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* FAQ
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* ===
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* Why should I use a dictionary?
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* ------------------------------
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*
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* Zstd can use dictionaries to improve compression ratio of small data.
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* Traditionally small files don't compress well because there is very little
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* repetition in a single sample, since it is small. But, if you are compressing
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* many similar files, like a bunch of JSON records that share the same
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* structure, you can train a dictionary on ahead of time on some samples of
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* these files. Then, zstd can use the dictionary to find repetitions that are
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* present across samples. This can vastly improve compression ratio.
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*
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* When is a dictionary useful?
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* ----------------------------
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*
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* Dictionaries are useful when compressing many small files that are similar.
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* The larger a file is, the less benefit a dictionary will have. Generally,
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* we don't expect dictionary compression to be effective past 100KB. And the
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* smaller a file is, the more we would expect the dictionary to help.
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*
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* How do I use a dictionary?
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* --------------------------
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*
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* Simply pass the dictionary to the zstd compressor with
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* `ZSTD_CCtx_loadDictionary()`. The same dictionary must then be passed to
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* the decompressor, using `ZSTD_DCtx_loadDictionary()`. There are other
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* more advanced functions that allow selecting some options, see zstd.h for
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* complete documentation.
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*
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* What is a zstd dictionary?
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* --------------------------
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*
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* A zstd dictionary has two pieces: Its header, and its content. The header
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* contains a magic number, the dictionary ID, and entropy tables. These
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* entropy tables allow zstd to save on header costs in the compressed file,
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* which really matters for small data. The content is just bytes, which are
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* repeated content that is common across many samples.
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*
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* What is a raw content dictionary?
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* ---------------------------------
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*
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* A raw content dictionary is just bytes. It doesn't have a zstd dictionary
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* header, a dictionary ID, or entropy tables. Any buffer is a valid raw
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* content dictionary.
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*
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* How do I train a dictionary?
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* ----------------------------
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*
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* Gather samples from your use case. These samples should be similar to each
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* other. If you have several use cases, you could try to train one dictionary
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* per use case.
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*
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* Pass those samples to `ZDICT_trainFromBuffer()` and that will train your
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* dictionary. There are a few advanced versions of this function, but this
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* is a great starting point. If you want to further tune your dictionary
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* you could try `ZDICT_optimizeTrainFromBuffer_cover()`. If that is too slow
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* you can try `ZDICT_optimizeTrainFromBuffer_fastCover()`.
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*
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* If the dictionary training function fails, that is likely because you
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* either passed too few samples, or a dictionary would not be effective
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* for your data. Look at the messages that the dictionary trainer printed,
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* if it doesn't say too few samples, then a dictionary would not be effective.
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*
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* How large should my dictionary be?
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* ----------------------------------
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*
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* A reasonable dictionary size, the `dictBufferCapacity`, is about 100KB.
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* The zstd CLI defaults to a 110KB dictionary. You likely don't need a
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* dictionary larger than that. But, most use cases can get away with a
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* smaller dictionary. The advanced dictionary builders can automatically
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* shrink the dictionary for you, and select a the smallest size that
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* doesn't hurt compression ratio too much. See the `shrinkDict` parameter.
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* A smaller dictionary can save memory, and potentially speed up
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* compression.
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*
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* How many samples should I provide to the dictionary builder?
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* ------------------------------------------------------------
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*
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* We generally recommend passing ~100x the size of the dictionary
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* in samples. A few thousand should suffice. Having too few samples
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* can hurt the dictionaries effectiveness. Having more samples will
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* only improve the dictionaries effectiveness. But having too many
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* samples can slow down the dictionary builder.
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*
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* How do I determine if a dictionary will be effective?
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* -----------------------------------------------------
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*
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* Simply train a dictionary and try it out. You can use zstd's built in
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* benchmarking tool to test the dictionary effectiveness.
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*
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* # Benchmark levels 1-3 without a dictionary
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* zstd -b1e3 -r /path/to/my/files
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* # Benchmark levels 1-3 with a dictionary
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* zstd -b1e3 -r /path/to/my/files -D /path/to/my/dictionary
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*
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* When should I retrain a dictionary?
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* -----------------------------------
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*
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* You should retrain a dictionary when its effectiveness drops. Dictionary
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* effectiveness drops as the data you are compressing changes. Generally, we do
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* expect dictionaries to "decay" over time, as your data changes, but the rate
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* at which they decay depends on your use case. Internally, we regularly
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* retrain dictionaries, and if the new dictionary performs significantly
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* better than the old dictionary, we will ship the new dictionary.
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*
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* I have a raw content dictionary, how do I turn it into a zstd dictionary?
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* -------------------------------------------------------------------------
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*
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* If you have a raw content dictionary, e.g. by manually constructing it, or
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* using a third-party dictionary builder, you can turn it into a zstd
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* dictionary by using `ZDICT_finalizeDictionary()`. You'll also have to
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* provide some samples of the data. It will add the zstd header to the
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* raw content, which contains a dictionary ID and entropy tables, which
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* will improve compression ratio, and allow zstd to write the dictionary ID
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* into the frame, if you so choose.
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*
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* Do I have to use zstd's dictionary builder?
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* -------------------------------------------
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*
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* No! You can construct dictionary content however you please, it is just
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* bytes. It will always be valid as a raw content dictionary. If you want
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* a zstd dictionary, which can improve compression ratio, use
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* `ZDICT_finalizeDictionary()`.
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*
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* What is the attack surface of a zstd dictionary?
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* ------------------------------------------------
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*
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* Zstd is heavily fuzz tested, including loading fuzzed dictionaries, so
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* zstd should never crash, or access out-of-bounds memory no matter what
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* the dictionary is. However, if an attacker can control the dictionary
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* during decompression, they can cause zstd to generate arbitrary bytes,
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* just like if they controlled the compressed data.
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*
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******************************************************************************/
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/*! ZDICT_trainFromBuffer():
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* Train a dictionary from an array of samples.
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* Redirect towards ZDICT_optimizeTrainFromBuffer_fastCover() single-threaded, with d=8, steps=4,
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* f=20, and accel=1.
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* Samples must be stored concatenated in a single flat buffer `samplesBuffer`,
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* supplied with an array of sizes `samplesSizes`, providing the size of each sample, in order.
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* The resulting dictionary will be saved into `dictBuffer`.
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* @return: size of dictionary stored into `dictBuffer` (<= `dictBufferCapacity`)
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* or an error code, which can be tested with ZDICT_isError().
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* Note: Dictionary training will fail if there are not enough samples to construct a
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* dictionary, or if most of the samples are too small (< 8 bytes being the lower limit).
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* If dictionary training fails, you should use zstd without a dictionary, as the dictionary
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* would've been ineffective anyways. If you believe your samples would benefit from a dictionary
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* please open an issue with details, and we can look into it.
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* Note: ZDICT_trainFromBuffer()'s memory usage is about 6 MB.
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* Tips: In general, a reasonable dictionary has a size of ~ 100 KB.
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* It's possible to select smaller or larger size, just by specifying `dictBufferCapacity`.
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* In general, it's recommended to provide a few thousands samples, though this can vary a lot.
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* It's recommended that total size of all samples be about ~x100 times the target size of dictionary.
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*/
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ZDICTLIB_API size_t ZDICT_trainFromBuffer(void* dictBuffer, size_t dictBufferCapacity,
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const void* samplesBuffer,
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const size_t* samplesSizes, unsigned nbSamples);
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typedef struct {
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int compressionLevel; /*< optimize for a specific zstd compression level; 0 means default */
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unsigned notificationLevel; /*< Write log to stderr; 0 = none (default); 1 = errors; 2 = progression; 3 = details; 4 = debug; */
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unsigned dictID; /*< force dictID value; 0 means auto mode (32-bits random value)
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* NOTE: The zstd format reserves some dictionary IDs for future use.
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* You may use them in private settings, but be warned that they
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* may be used by zstd in a public dictionary registry in the future.
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* These dictionary IDs are:
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* - low range : <= 32767
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* - high range : >= (2^31)
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*/
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} ZDICT_params_t;
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/*! ZDICT_finalizeDictionary():
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* Given a custom content as a basis for dictionary, and a set of samples,
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* finalize dictionary by adding headers and statistics according to the zstd
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* dictionary format.
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*
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* Samples must be stored concatenated in a flat buffer `samplesBuffer`,
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* supplied with an array of sizes `samplesSizes`, providing the size of each
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* sample in order. The samples are used to construct the statistics, so they
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* should be representative of what you will compress with this dictionary.
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*
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* The compression level can be set in `parameters`. You should pass the
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* compression level you expect to use in production. The statistics for each
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* compression level differ, so tuning the dictionary for the compression level
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* can help quite a bit.
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*
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* You can set an explicit dictionary ID in `parameters`, or allow us to pick
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* a random dictionary ID for you, but we can't guarantee no collisions.
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*
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* The dstDictBuffer and the dictContent may overlap, and the content will be
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* appended to the end of the header. If the header + the content doesn't fit in
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* maxDictSize the beginning of the content is truncated to make room, since it
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* is presumed that the most profitable content is at the end of the dictionary,
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* since that is the cheapest to reference.
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*
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* `maxDictSize` must be >= max(dictContentSize, ZSTD_DICTSIZE_MIN).
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*
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* @return: size of dictionary stored into `dstDictBuffer` (<= `maxDictSize`),
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* or an error code, which can be tested by ZDICT_isError().
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* Note: ZDICT_finalizeDictionary() will push notifications into stderr if
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* instructed to, using notificationLevel>0.
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* NOTE: This function currently may fail in several edge cases including:
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* * Not enough samples
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* * Samples are uncompressible
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* * Samples are all exactly the same
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*/
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ZDICTLIB_API size_t ZDICT_finalizeDictionary(void* dstDictBuffer, size_t maxDictSize,
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const void* dictContent, size_t dictContentSize,
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const void* samplesBuffer, const size_t* samplesSizes, unsigned nbSamples,
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ZDICT_params_t parameters);
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/*====== Helper functions ======*/
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ZDICTLIB_API unsigned ZDICT_getDictID(const void* dictBuffer, size_t dictSize); /**< extracts dictID; @return zero if error (not a valid dictionary) */
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ZDICTLIB_API size_t ZDICT_getDictHeaderSize(const void* dictBuffer, size_t dictSize); /* returns dict header size; returns a ZSTD error code on failure */
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ZDICTLIB_API unsigned ZDICT_isError(size_t errorCode);
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ZDICTLIB_API const char* ZDICT_getErrorName(size_t errorCode);
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#ifdef ZDICT_STATIC_LINKING_ONLY
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/* ====================================================================================
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* The definitions in this section are considered experimental.
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* They should never be used with a dynamic library, as they may change in the future.
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* They are provided for advanced usages.
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* Use them only in association with static linking.
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* ==================================================================================== */
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#define ZDICT_DICTSIZE_MIN 256
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/* Deprecated: Remove in v1.6.0 */
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#define ZDICT_CONTENTSIZE_MIN 128
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/*! ZDICT_cover_params_t:
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* k and d are the only required parameters.
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* For others, value 0 means default.
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*/
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typedef struct {
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unsigned k; /* Segment size : constraint: 0 < k : Reasonable range [16, 2048+] */
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unsigned d; /* dmer size : constraint: 0 < d <= k : Reasonable range [6, 16] */
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unsigned steps; /* Number of steps : Only used for optimization : 0 means default (40) : Higher means more parameters checked */
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unsigned nbThreads; /* Number of threads : constraint: 0 < nbThreads : 1 means single-threaded : Only used for optimization : Ignored if ZSTD_MULTITHREAD is not defined */
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double splitPoint; /* Percentage of samples used for training: Only used for optimization : the first nbSamples * splitPoint samples will be used to training, the last nbSamples * (1 - splitPoint) samples will be used for testing, 0 means default (1.0), 1.0 when all samples are used for both training and testing */
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unsigned shrinkDict; /* Train dictionaries to shrink in size starting from the minimum size and selects the smallest dictionary that is shrinkDictMaxRegression% worse than the largest dictionary. 0 means no shrinking and 1 means shrinking */
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unsigned shrinkDictMaxRegression; /* Sets shrinkDictMaxRegression so that a smaller dictionary can be at worse shrinkDictMaxRegression% worse than the max dict size dictionary. */
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ZDICT_params_t zParams;
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} ZDICT_cover_params_t;
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typedef struct {
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unsigned k; /* Segment size : constraint: 0 < k : Reasonable range [16, 2048+] */
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unsigned d; /* dmer size : constraint: 0 < d <= k : Reasonable range [6, 16] */
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unsigned f; /* log of size of frequency array : constraint: 0 < f <= 31 : 1 means default(20)*/
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unsigned steps; /* Number of steps : Only used for optimization : 0 means default (40) : Higher means more parameters checked */
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unsigned nbThreads; /* Number of threads : constraint: 0 < nbThreads : 1 means single-threaded : Only used for optimization : Ignored if ZSTD_MULTITHREAD is not defined */
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double splitPoint; /* Percentage of samples used for training: Only used for optimization : the first nbSamples * splitPoint samples will be used to training, the last nbSamples * (1 - splitPoint) samples will be used for testing, 0 means default (0.75), 1.0 when all samples are used for both training and testing */
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unsigned accel; /* Acceleration level: constraint: 0 < accel <= 10, higher means faster and less accurate, 0 means default(1) */
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unsigned shrinkDict; /* Train dictionaries to shrink in size starting from the minimum size and selects the smallest dictionary that is shrinkDictMaxRegression% worse than the largest dictionary. 0 means no shrinking and 1 means shrinking */
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unsigned shrinkDictMaxRegression; /* Sets shrinkDictMaxRegression so that a smaller dictionary can be at worse shrinkDictMaxRegression% worse than the max dict size dictionary. */
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ZDICT_params_t zParams;
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} ZDICT_fastCover_params_t;
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/*! ZDICT_trainFromBuffer_cover():
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* Train a dictionary from an array of samples using the COVER algorithm.
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* Samples must be stored concatenated in a single flat buffer `samplesBuffer`,
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* supplied with an array of sizes `samplesSizes`, providing the size of each sample, in order.
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* The resulting dictionary will be saved into `dictBuffer`.
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* @return: size of dictionary stored into `dictBuffer` (<= `dictBufferCapacity`)
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* or an error code, which can be tested with ZDICT_isError().
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* See ZDICT_trainFromBuffer() for details on failure modes.
|
||
|
|
* Note: ZDICT_trainFromBuffer_cover() requires about 9 bytes of memory for each input byte.
|
||
|
|
* Tips: In general, a reasonable dictionary has a size of ~ 100 KB.
|
||
|
|
* It's possible to select smaller or larger size, just by specifying `dictBufferCapacity`.
|
||
|
|
* In general, it's recommended to provide a few thousands samples, though this can vary a lot.
|
||
|
|
* It's recommended that total size of all samples be about ~x100 times the target size of dictionary.
|
||
|
|
*/
|
||
|
|
ZDICTLIB_API size_t ZDICT_trainFromBuffer_cover(
|
||
|
|
void *dictBuffer, size_t dictBufferCapacity,
|
||
|
|
const void *samplesBuffer, const size_t *samplesSizes, unsigned nbSamples,
|
||
|
|
ZDICT_cover_params_t parameters);
|
||
|
|
|
||
|
|
/*! ZDICT_optimizeTrainFromBuffer_cover():
|
||
|
|
* The same requirements as above hold for all the parameters except `parameters`.
|
||
|
|
* This function tries many parameter combinations and picks the best parameters.
|
||
|
|
* `*parameters` is filled with the best parameters found,
|
||
|
|
* dictionary constructed with those parameters is stored in `dictBuffer`.
|
||
|
|
*
|
||
|
|
* All of the parameters d, k, steps are optional.
|
||
|
|
* If d is non-zero then we don't check multiple values of d, otherwise we check d = {6, 8}.
|
||
|
|
* if steps is zero it defaults to its default value.
|
||
|
|
* If k is non-zero then we don't check multiple values of k, otherwise we check steps values in [50, 2000].
|
||
|
|
*
|
||
|
|
* @return: size of dictionary stored into `dictBuffer` (<= `dictBufferCapacity`)
|
||
|
|
* or an error code, which can be tested with ZDICT_isError().
|
||
|
|
* On success `*parameters` contains the parameters selected.
|
||
|
|
* See ZDICT_trainFromBuffer() for details on failure modes.
|
||
|
|
* Note: ZDICT_optimizeTrainFromBuffer_cover() requires about 8 bytes of memory for each input byte and additionally another 5 bytes of memory for each byte of memory for each thread.
|
||
|
|
*/
|
||
|
|
ZDICTLIB_API size_t ZDICT_optimizeTrainFromBuffer_cover(
|
||
|
|
void* dictBuffer, size_t dictBufferCapacity,
|
||
|
|
const void* samplesBuffer, const size_t* samplesSizes, unsigned nbSamples,
|
||
|
|
ZDICT_cover_params_t* parameters);
|
||
|
|
|
||
|
|
/*! ZDICT_trainFromBuffer_fastCover():
|
||
|
|
* Train a dictionary from an array of samples using a modified version of COVER algorithm.
|
||
|
|
* Samples must be stored concatenated in a single flat buffer `samplesBuffer`,
|
||
|
|
* supplied with an array of sizes `samplesSizes`, providing the size of each sample, in order.
|
||
|
|
* d and k are required.
|
||
|
|
* All other parameters are optional, will use default values if not provided
|
||
|
|
* The resulting dictionary will be saved into `dictBuffer`.
|
||
|
|
* @return: size of dictionary stored into `dictBuffer` (<= `dictBufferCapacity`)
|
||
|
|
* or an error code, which can be tested with ZDICT_isError().
|
||
|
|
* See ZDICT_trainFromBuffer() for details on failure modes.
|
||
|
|
* Note: ZDICT_trainFromBuffer_fastCover() requires 6 * 2^f bytes of memory.
|
||
|
|
* Tips: In general, a reasonable dictionary has a size of ~ 100 KB.
|
||
|
|
* It's possible to select smaller or larger size, just by specifying `dictBufferCapacity`.
|
||
|
|
* In general, it's recommended to provide a few thousands samples, though this can vary a lot.
|
||
|
|
* It's recommended that total size of all samples be about ~x100 times the target size of dictionary.
|
||
|
|
*/
|
||
|
|
ZDICTLIB_API size_t ZDICT_trainFromBuffer_fastCover(void *dictBuffer,
|
||
|
|
size_t dictBufferCapacity, const void *samplesBuffer,
|
||
|
|
const size_t *samplesSizes, unsigned nbSamples,
|
||
|
|
ZDICT_fastCover_params_t parameters);
|
||
|
|
|
||
|
|
/*! ZDICT_optimizeTrainFromBuffer_fastCover():
|
||
|
|
* The same requirements as above hold for all the parameters except `parameters`.
|
||
|
|
* This function tries many parameter combinations (specifically, k and d combinations)
|
||
|
|
* and picks the best parameters. `*parameters` is filled with the best parameters found,
|
||
|
|
* dictionary constructed with those parameters is stored in `dictBuffer`.
|
||
|
|
* All of the parameters d, k, steps, f, and accel are optional.
|
||
|
|
* If d is non-zero then we don't check multiple values of d, otherwise we check d = {6, 8}.
|
||
|
|
* if steps is zero it defaults to its default value.
|
||
|
|
* If k is non-zero then we don't check multiple values of k, otherwise we check steps values in [50, 2000].
|
||
|
|
* If f is zero, default value of 20 is used.
|
||
|
|
* If accel is zero, default value of 1 is used.
|
||
|
|
*
|
||
|
|
* @return: size of dictionary stored into `dictBuffer` (<= `dictBufferCapacity`)
|
||
|
|
* or an error code, which can be tested with ZDICT_isError().
|
||
|
|
* On success `*parameters` contains the parameters selected.
|
||
|
|
* See ZDICT_trainFromBuffer() for details on failure modes.
|
||
|
|
* Note: ZDICT_optimizeTrainFromBuffer_fastCover() requires about 6 * 2^f bytes of memory for each thread.
|
||
|
|
*/
|
||
|
|
ZDICTLIB_API size_t ZDICT_optimizeTrainFromBuffer_fastCover(void* dictBuffer,
|
||
|
|
size_t dictBufferCapacity, const void* samplesBuffer,
|
||
|
|
const size_t* samplesSizes, unsigned nbSamples,
|
||
|
|
ZDICT_fastCover_params_t* parameters);
|
||
|
|
|
||
|
|
typedef struct {
|
||
|
|
unsigned selectivityLevel; /* 0 means default; larger => select more => larger dictionary */
|
||
|
|
ZDICT_params_t zParams;
|
||
|
|
} ZDICT_legacy_params_t;
|
||
|
|
|
||
|
|
/*! ZDICT_trainFromBuffer_legacy():
|
||
|
|
* Train a dictionary from an array of samples.
|
||
|
|
* Samples must be stored concatenated in a single flat buffer `samplesBuffer`,
|
||
|
|
* supplied with an array of sizes `samplesSizes`, providing the size of each sample, in order.
|
||
|
|
* The resulting dictionary will be saved into `dictBuffer`.
|
||
|
|
* `parameters` is optional and can be provided with values set to 0 to mean "default".
|
||
|
|
* @return: size of dictionary stored into `dictBuffer` (<= `dictBufferCapacity`)
|
||
|
|
* or an error code, which can be tested with ZDICT_isError().
|
||
|
|
* See ZDICT_trainFromBuffer() for details on failure modes.
|
||
|
|
* Tips: In general, a reasonable dictionary has a size of ~ 100 KB.
|
||
|
|
* It's possible to select smaller or larger size, just by specifying `dictBufferCapacity`.
|
||
|
|
* In general, it's recommended to provide a few thousands samples, though this can vary a lot.
|
||
|
|
* It's recommended that total size of all samples be about ~x100 times the target size of dictionary.
|
||
|
|
* Note: ZDICT_trainFromBuffer_legacy() will send notifications into stderr if instructed to, using notificationLevel>0.
|
||
|
|
*/
|
||
|
|
ZDICTLIB_API size_t ZDICT_trainFromBuffer_legacy(
|
||
|
|
void* dictBuffer, size_t dictBufferCapacity,
|
||
|
|
const void* samplesBuffer, const size_t* samplesSizes, unsigned nbSamples,
|
||
|
|
ZDICT_legacy_params_t parameters);
|
||
|
|
|
||
|
|
|
||
|
|
/* Deprecation warnings */
|
||
|
|
/* It is generally possible to disable deprecation warnings from compiler,
|
||
|
|
for example with -Wno-deprecated-declarations for gcc
|
||
|
|
or _CRT_SECURE_NO_WARNINGS in Visual.
|
||
|
|
Otherwise, it's also possible to manually define ZDICT_DISABLE_DEPRECATE_WARNINGS */
|
||
|
|
#ifdef ZDICT_DISABLE_DEPRECATE_WARNINGS
|
||
|
|
# define ZDICT_DEPRECATED(message) ZDICTLIB_API /* disable deprecation warnings */
|
||
|
|
#else
|
||
|
|
# define ZDICT_GCC_VERSION (__GNUC__ * 100 + __GNUC_MINOR__)
|
||
|
|
# if defined (__cplusplus) && (__cplusplus >= 201402) /* C++14 or greater */
|
||
|
|
# define ZDICT_DEPRECATED(message) [[deprecated(message)]] ZDICTLIB_API
|
||
|
|
# elif defined(__clang__) || (ZDICT_GCC_VERSION >= 405)
|
||
|
|
# define ZDICT_DEPRECATED(message) ZDICTLIB_API __attribute__((deprecated(message)))
|
||
|
|
# elif (ZDICT_GCC_VERSION >= 301)
|
||
|
|
# define ZDICT_DEPRECATED(message) ZDICTLIB_API __attribute__((deprecated))
|
||
|
|
# elif defined(_MSC_VER)
|
||
|
|
# define ZDICT_DEPRECATED(message) ZDICTLIB_API __declspec(deprecated(message))
|
||
|
|
# else
|
||
|
|
# pragma message("WARNING: You need to implement ZDICT_DEPRECATED for this compiler")
|
||
|
|
# define ZDICT_DEPRECATED(message) ZDICTLIB_API
|
||
|
|
# endif
|
||
|
|
#endif /* ZDICT_DISABLE_DEPRECATE_WARNINGS */
|
||
|
|
|
||
|
|
ZDICT_DEPRECATED("use ZDICT_finalizeDictionary() instead")
|
||
|
|
size_t ZDICT_addEntropyTablesFromBuffer(void* dictBuffer, size_t dictContentSize, size_t dictBufferCapacity,
|
||
|
|
const void* samplesBuffer, const size_t* samplesSizes, unsigned nbSamples);
|
||
|
|
|
||
|
|
|
||
|
|
#endif /* ZDICT_STATIC_LINKING_ONLY */
|
||
|
|
|
||
|
|
#if defined (__cplusplus)
|
||
|
|
}
|
||
|
|
#endif
|
||
|
|
|
||
|
|
#endif /* DICTBUILDER_H_001 */
|