contrib/python-zstandard/tests/common.py
author Gregory Szorc <gregory.szorc@gmail.com>
Mon, 09 Apr 2018 10:13:29 -0700
changeset 37495 b1fb341d8a61
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child 42070 675775c33ab6
permissions -rw-r--r--
zstandard: vendor python-zstandard 0.9.0 This was just released. It features a number of goodies. More info at https://gregoryszorc.com/blog/2018/04/09/release-of-python-zstandard-0.9/. The clang-format ignore list was updated to reflect the new source of files. The project contains a vendored copy of zstandard 1.3.4. The old version was 1.1.3. One of the changes between those versions is that zstandard is now dual licensed BSD + GPLv2 and the patent rights grant has been removed. Good riddance. The API should be backwards compatible. So no changes in core should be needed. However, there were a number of changes in the library that we'll want to adapt to. Those will be addressed in subsequent commits. Differential Revision: https://phab.mercurial-scm.org/D3198
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import imp
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import inspect
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import io
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import os
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import types
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try:
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    import hypothesis
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except ImportError:
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    hypothesis = None
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def make_cffi(cls):
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    """Decorator to add CFFI versions of each test method."""
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    # The module containing this class definition should
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    # `import zstandard as zstd`. Otherwise things may blow up.
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    mod = inspect.getmodule(cls)
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    if not hasattr(mod, 'zstd'):
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        raise Exception('test module does not contain "zstd" symbol')
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    if not hasattr(mod.zstd, 'backend'):
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        raise Exception('zstd symbol does not have "backend" attribute; did '
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                        'you `import zstandard as zstd`?')
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    # If `import zstandard` already chose the cffi backend, there is nothing
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    # for us to do: we only add the cffi variation if the default backend
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    # is the C extension.
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    if mod.zstd.backend == 'cffi':
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        return cls
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    old_env = dict(os.environ)
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    os.environ['PYTHON_ZSTANDARD_IMPORT_POLICY'] = 'cffi'
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    try:
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        try:
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            mod_info = imp.find_module('zstandard')
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            mod = imp.load_module('zstandard_cffi', *mod_info)
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        except ImportError:
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            return cls
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    finally:
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        os.environ.clear()
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        os.environ.update(old_env)
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    if mod.backend != 'cffi':
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        raise Exception('got the zstandard %s backend instead of cffi' % mod.backend)
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    # If CFFI version is available, dynamically construct test methods
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    # that use it.
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    for attr in dir(cls):
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        fn = getattr(cls, attr)
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        if not inspect.ismethod(fn) and not inspect.isfunction(fn):
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            continue
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        if not fn.__name__.startswith('test_'):
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            continue
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        name = '%s_cffi' % fn.__name__
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        # Replace the "zstd" symbol with the CFFI module instance. Then copy
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        # the function object and install it in a new attribute.
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        if isinstance(fn, types.FunctionType):
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            globs = dict(fn.__globals__)
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            globs['zstd'] = mod
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            new_fn = types.FunctionType(fn.__code__, globs, name,
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                                        fn.__defaults__, fn.__closure__)
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            new_method = new_fn
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        else:
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            globs = dict(fn.__func__.func_globals)
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            globs['zstd'] = mod
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            new_fn = types.FunctionType(fn.__func__.func_code, globs, name,
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                                        fn.__func__.func_defaults,
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                                        fn.__func__.func_closure)
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            new_method = types.UnboundMethodType(new_fn, fn.im_self,
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                                                 fn.im_class)
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        setattr(cls, name, new_method)
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    return cls
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class OpCountingBytesIO(io.BytesIO):
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    def __init__(self, *args, **kwargs):
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        self._read_count = 0
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        self._write_count = 0
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        return super(OpCountingBytesIO, self).__init__(*args, **kwargs)
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    def read(self, *args):
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        self._read_count += 1
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        return super(OpCountingBytesIO, self).read(*args)
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    def write(self, data):
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        self._write_count += 1
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        return super(OpCountingBytesIO, self).write(data)
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_source_files = []
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def random_input_data():
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    """Obtain the raw content of source files.
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    This is used for generating "random" data to feed into fuzzing, since it is
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    faster than random content generation.
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    """
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    if _source_files:
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        return _source_files
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    for root, dirs, files in os.walk(os.path.dirname(__file__)):
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        dirs[:] = list(sorted(dirs))
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        for f in sorted(files):
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            try:
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                with open(os.path.join(root, f), 'rb') as fh:
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                    data = fh.read()
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                    if data:
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                        _source_files.append(data)
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            except OSError:
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                pass
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    return _source_files
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def generate_samples():
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    inputs = [
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        b'foo',
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        b'bar',
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        b'abcdef',
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        b'sometext',
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        b'baz',
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    ]
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    samples = []
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    for i in range(128):
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        samples.append(inputs[i % 5])
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        samples.append(inputs[i % 5] * (i + 3))
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        samples.append(inputs[-(i % 5)] * (i + 2))
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    return samples
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if hypothesis:
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    default_settings = hypothesis.settings()
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    hypothesis.settings.register_profile('default', default_settings)
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    ci_settings = hypothesis.settings(max_examples=2500,
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                                      max_iterations=2500)
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    hypothesis.settings.register_profile('ci', ci_settings)
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    hypothesis.settings.load_profile(
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        os.environ.get('HYPOTHESIS_PROFILE', 'default'))