contrib/python-zstandard/tests/common.py

revlogutils: teach issue6528 filtering about grandparents

During dynamic filtering, we should assume that the current repository
is correct. Therefore the parents of the delta base can tell us if that
parent has metadata without having to build the whole text.

import imp
import inspect
import io
import os
import types
import unittest

try:
    import hypothesis
except ImportError:
    hypothesis = None


class TestCase(unittest.TestCase):
    if not getattr(unittest.TestCase, "assertRaisesRegex", False):
        assertRaisesRegex = unittest.TestCase.assertRaisesRegexp


def make_cffi(cls):
    """Decorator to add CFFI versions of each test method."""

    # The module containing this class definition should
    # `import zstandard as zstd`. Otherwise things may blow up.
    mod = inspect.getmodule(cls)
    if not hasattr(mod, "zstd"):
        raise Exception('test module does not contain "zstd" symbol')

    if not hasattr(mod.zstd, "backend"):
        raise Exception(
            'zstd symbol does not have "backend" attribute; did '
            "you `import zstandard as zstd`?"
        )

    # If `import zstandard` already chose the cffi backend, there is nothing
    # for us to do: we only add the cffi variation if the default backend
    # is the C extension.
    if mod.zstd.backend == "cffi":
        return cls

    old_env = dict(os.environ)
    os.environ["PYTHON_ZSTANDARD_IMPORT_POLICY"] = "cffi"
    try:
        try:
            mod_info = imp.find_module("zstandard")
            mod = imp.load_module("zstandard_cffi", *mod_info)
        except ImportError:
            return cls
    finally:
        os.environ.clear()
        os.environ.update(old_env)

    if mod.backend != "cffi":
        raise Exception(
            "got the zstandard %s backend instead of cffi" % mod.backend
        )

    # If CFFI version is available, dynamically construct test methods
    # that use it.

    for attr in dir(cls):
        fn = getattr(cls, attr)
        if not inspect.ismethod(fn) and not inspect.isfunction(fn):
            continue

        if not fn.__name__.startswith("test_"):
            continue

        name = "%s_cffi" % fn.__name__

        # Replace the "zstd" symbol with the CFFI module instance. Then copy
        # the function object and install it in a new attribute.
        if isinstance(fn, types.FunctionType):
            globs = dict(fn.__globals__)
            globs["zstd"] = mod
            new_fn = types.FunctionType(
                fn.__code__, globs, name, fn.__defaults__, fn.__closure__
            )
            new_method = new_fn
        else:
            globs = dict(fn.__func__.func_globals)
            globs["zstd"] = mod
            new_fn = types.FunctionType(
                fn.__func__.func_code,
                globs,
                name,
                fn.__func__.func_defaults,
                fn.__func__.func_closure,
            )
            new_method = types.UnboundMethodType(
                new_fn, fn.im_self, fn.im_class
            )

        setattr(cls, name, new_method)

    return cls


class NonClosingBytesIO(io.BytesIO):
    """BytesIO that saves the underlying buffer on close().

    This allows us to access written data after close().
    """

    def __init__(self, *args, **kwargs):
        super(NonClosingBytesIO, self).__init__(*args, **kwargs)
        self._saved_buffer = None

    def close(self):
        self._saved_buffer = self.getvalue()
        return super(NonClosingBytesIO, self).close()

    def getvalue(self):
        if self.closed:
            return self._saved_buffer
        else:
            return super(NonClosingBytesIO, self).getvalue()


class OpCountingBytesIO(NonClosingBytesIO):
    def __init__(self, *args, **kwargs):
        self._flush_count = 0
        self._read_count = 0
        self._write_count = 0
        return super(OpCountingBytesIO, self).__init__(*args, **kwargs)

    def flush(self):
        self._flush_count += 1
        return super(OpCountingBytesIO, self).flush()

    def read(self, *args):
        self._read_count += 1
        return super(OpCountingBytesIO, self).read(*args)

    def write(self, data):
        self._write_count += 1
        return super(OpCountingBytesIO, self).write(data)


_source_files = []


def random_input_data():
    """Obtain the raw content of source files.

    This is used for generating "random" data to feed into fuzzing, since it is
    faster than random content generation.
    """
    if _source_files:
        return _source_files

    for root, dirs, files in os.walk(os.path.dirname(__file__)):
        dirs[:] = list(sorted(dirs))
        for f in sorted(files):
            try:
                with open(os.path.join(root, f), "rb") as fh:
                    data = fh.read()
                    if data:
                        _source_files.append(data)
            except OSError:
                pass

    # Also add some actual random data.
    _source_files.append(os.urandom(100))
    _source_files.append(os.urandom(1000))
    _source_files.append(os.urandom(10000))
    _source_files.append(os.urandom(100000))
    _source_files.append(os.urandom(1000000))

    return _source_files


def generate_samples():
    inputs = [
        b"foo",
        b"bar",
        b"abcdef",
        b"sometext",
        b"baz",
    ]

    samples = []

    for i in range(128):
        samples.append(inputs[i % 5])
        samples.append(inputs[i % 5] * (i + 3))
        samples.append(inputs[-(i % 5)] * (i + 2))

    return samples


if hypothesis:
    default_settings = hypothesis.settings(deadline=10000)
    hypothesis.settings.register_profile("default", default_settings)

    ci_settings = hypothesis.settings(deadline=20000, max_examples=1000)
    hypothesis.settings.register_profile("ci", ci_settings)

    expensive_settings = hypothesis.settings(deadline=None, max_examples=10000)
    hypothesis.settings.register_profile("expensive", expensive_settings)

    hypothesis.settings.load_profile(
        os.environ.get("HYPOTHESIS_PROFILE", "default")
    )