import imp

import inspect

import io

import os

import types

try:

    import hypothesis

except ImportError:

    hypothesis = None

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

    def __init__(self, *args, **kwargs):

        self._read_count = 0

        self._write_count = 0

        return super(OpCountingBytesIO, self).__init__(*args, **kwargs)

    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

    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:

    hypothesis.settings.register_profile('default', default_settings)

    hypothesis.settings.register_profile('ci', ci_settings)

    hypothesis.settings.load_profile(

        os.environ.get('HYPOTHESIS_PROFILE', 'default'))