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zstd: vendor python-zstandard 0.5.0...
zstd: vendor python-zstandard 0.5.0 As the commit message for the previous changeset says, we wish for zstd to be a 1st class citizen in Mercurial. To make that happen, we need to enable Python to talk to the zstd C API. And that requires bindings. This commit vendors a copy of existing Python bindings. Why do we need to vendor? As the commit message of the previous commit says, relying on systems in the wild to have the bindings or zstd present is a losing proposition. By distributing the zstd and bindings with Mercurial, we significantly increase our chances that zstd will work. Since zstd will deliver a better end-user experience by achieving better performance, this benefits our users. Another reason is that the Python bindings still aren't stable and the API is somewhat fluid. While Mercurial could be coded to target multiple versions of the Python bindings, it is safer to bundle an explicit, known working version. The added Python bindings are mostly a fully-featured interface to the zstd C API. They allow one-shot operations, streaming, reading and writing from objects implements the file object protocol, dictionary compression, control over low-level compression parameters, and more. The Python bindings work on Python 2.6, 2.7, and 3.3+ and have been tested on Linux and Windows. There are CFFI bindings, but they are lacking compared to the C extension. Upstream work will be needed before we can support zstd with PyPy. But it will be possible. The files added in this commit come from Git commit e637c1b214d5f869cf8116c550dcae23ec13b677 from https://github.com/indygreg/python-zstandard and are added without modifications. Some files from the upstream repository have been omitted, namely files related to continuous integration. In the spirit of full disclosure, I'm the maintainer of the "python-zstandard" project and have authored 100% of the code added in this commit. Unfortunately, the Python bindings have not been formally code reviewed by anyone. While I've tested much of the code thoroughly (I even have tests that fuzz APIs), there's a good chance there are bugs, memory leaks, not well thought out APIs, etc. If someone wants to review the code and send feedback to the GitHub project, it would be greatly appreciated. Despite my involvement with both projects, my opinions of code style differ from Mercurial's. The code in this commit introduces numerous code style violations in Mercurial's linters. So, the code is excluded from most lints. However, some violations I agree with. These have been added to the known violations ignore list for now.

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test_data_structures.py
107 lines | 5.1 KiB | text/x-python | PythonLexer
import io
try:
import unittest2 as unittest
except ImportError:
import unittest
try:
import hypothesis
import hypothesis.strategies as strategies
except ImportError:
hypothesis = None
import zstd
class TestCompressionParameters(unittest.TestCase):
def test_init_bad_arg_type(self):
with self.assertRaises(TypeError):
zstd.CompressionParameters()
with self.assertRaises(TypeError):
zstd.CompressionParameters(0, 1)
def test_bounds(self):
zstd.CompressionParameters(zstd.WINDOWLOG_MIN,
zstd.CHAINLOG_MIN,
zstd.HASHLOG_MIN,
zstd.SEARCHLOG_MIN,
zstd.SEARCHLENGTH_MIN,
zstd.TARGETLENGTH_MIN,
zstd.STRATEGY_FAST)
zstd.CompressionParameters(zstd.WINDOWLOG_MAX,
zstd.CHAINLOG_MAX,
zstd.HASHLOG_MAX,
zstd.SEARCHLOG_MAX,
zstd.SEARCHLENGTH_MAX,
zstd.TARGETLENGTH_MAX,
zstd.STRATEGY_BTOPT)
def test_get_compression_parameters(self):
p = zstd.get_compression_parameters(1)
self.assertIsInstance(p, zstd.CompressionParameters)
self.assertEqual(p[0], 19)
if hypothesis:
s_windowlog = strategies.integers(min_value=zstd.WINDOWLOG_MIN,
max_value=zstd.WINDOWLOG_MAX)
s_chainlog = strategies.integers(min_value=zstd.CHAINLOG_MIN,
max_value=zstd.CHAINLOG_MAX)
s_hashlog = strategies.integers(min_value=zstd.HASHLOG_MIN,
max_value=zstd.HASHLOG_MAX)
s_searchlog = strategies.integers(min_value=zstd.SEARCHLOG_MIN,
max_value=zstd.SEARCHLOG_MAX)
s_searchlength = strategies.integers(min_value=zstd.SEARCHLENGTH_MIN,
max_value=zstd.SEARCHLENGTH_MAX)
s_targetlength = strategies.integers(min_value=zstd.TARGETLENGTH_MIN,
max_value=zstd.TARGETLENGTH_MAX)
s_strategy = strategies.sampled_from((zstd.STRATEGY_FAST,
zstd.STRATEGY_DFAST,
zstd.STRATEGY_GREEDY,
zstd.STRATEGY_LAZY,
zstd.STRATEGY_LAZY2,
zstd.STRATEGY_BTLAZY2,
zstd.STRATEGY_BTOPT))
class TestCompressionParametersHypothesis(unittest.TestCase):
@hypothesis.given(s_windowlog, s_chainlog, s_hashlog, s_searchlog,
s_searchlength, s_targetlength, s_strategy)
def test_valid_init(self, windowlog, chainlog, hashlog, searchlog,
searchlength, targetlength, strategy):
p = zstd.CompressionParameters(windowlog, chainlog, hashlog,
searchlog, searchlength,
targetlength, strategy)
self.assertEqual(tuple(p),
(windowlog, chainlog, hashlog, searchlog,
searchlength, targetlength, strategy))
# Verify we can instantiate a compressor with the supplied values.
# ZSTD_checkCParams moves the goal posts on us from what's advertised
# in the constants. So move along with them.
if searchlength == zstd.SEARCHLENGTH_MIN and strategy in (zstd.STRATEGY_FAST, zstd.STRATEGY_GREEDY):
searchlength += 1
p = zstd.CompressionParameters(windowlog, chainlog, hashlog,
searchlog, searchlength,
targetlength, strategy)
elif searchlength == zstd.SEARCHLENGTH_MAX and strategy != zstd.STRATEGY_FAST:
searchlength -= 1
p = zstd.CompressionParameters(windowlog, chainlog, hashlog,
searchlog, searchlength,
targetlength, strategy)
cctx = zstd.ZstdCompressor(compression_params=p)
with cctx.write_to(io.BytesIO()):
pass
@hypothesis.given(s_windowlog, s_chainlog, s_hashlog, s_searchlog,
s_searchlength, s_targetlength, s_strategy)
def test_estimate_compression_context_size(self, windowlog, chainlog,
hashlog, searchlog,
searchlength, targetlength,
strategy):
p = zstd.CompressionParameters(windowlog, chainlog, hashlog,
searchlog, searchlength,
targetlength, strategy)
size = zstd.estimate_compression_context_size(p)