ddt.py

# -*- coding: utf-8 -*-
# This file is a part of DDT (https://github.com/datadriventests/ddt)
# Copyright 2012-2015 Carles Barrobés and DDT contributors
# For the exact contribution history, see the git revision log.
# DDT is licensed under the MIT License, included in
# https://github.com/datadriventests/ddt/blob/master/LICENSE.md

import codecs
import inspect
import json
import os
import re
from enum import Enum, unique
from functools import wraps

try:
    import yaml
except ImportError:  # pragma: no cover
    _have_yaml = False
else:
    _have_yaml = True

__version__ = '1.5.0'

# These attributes will not conflict with any real python attribute
# They are added to the decorated test method and processed later
# by the `ddt` class decorator.

DATA_ATTR = '%values'              # store the data the test must run with
FILE_ATTR = '%file_path'           # store the path to JSON file
YAML_LOADER_ATTR = '%yaml_loader'  # store custom yaml loader for serialization
UNPACK_ATTR = '%unpack'            # remember that we have to unpack values
INDEX_LEN = '%index_len'           # store the index length of the data


# These are helper classes for @named_data that allow ddt tests to have meaningful names.
class NamedDataList(list):
    def __init__(self, name, *args):
        super(NamedDataList, self).__init__(args)
        self.name = name

    def __str__(self):
        return str(self.name)


class NamedDataDict(dict):
    def __init__(self, name, **kwargs):
        super(NamedDataDict, self).__init__(kwargs)
        self.name = name

    def __str__(self):
        return str(self.name)


trivial_types = (type(None), bool, int, float, NamedDataList, NamedDataDict)
try:
    trivial_types += (basestring, )
except NameError:
    trivial_types += (str, )


@unique
class TestNameFormat(Enum):
    """
    An enum to configure how ``mk_test_name()`` to compose a test name.  Given
    the following example:

    .. code-block:: python

        @data("a", "b")
        def testSomething(self, value):
            ...

    if using just ``@ddt`` or together with ``DEFAULT``:

    * ``testSomething_1_a``
    * ``testSomething_2_b``

    if using ``INDEX_ONLY``:

    * ``testSomething_1``
    * ``testSomething_2``

    """
    DEFAULT = 0
    INDEX_ONLY = 1


def is_trivial(value):
    if isinstance(value, trivial_types):
        return True
    elif isinstance(value, (list, tuple)):
        return all(map(is_trivial, value))
    return False


def unpack(func):
    """
    Method decorator to add unpack feature.

    """
    setattr(func, UNPACK_ATTR, True)
    return func


def data(*values):
    """
    Method decorator to add to your test methods.

    Should be added to methods of instances of ``unittest.TestCase``.

    """
    return idata(values)


def idata(iterable, index_len=None):
    """
    Method decorator to add to your test methods.

    Should be added to methods of instances of ``unittest.TestCase``.

    :param iterable: iterable of the values to provide to the test function.
    :param index_len: an optional integer specifying the width to zero-pad the
        test identifier indices to.  If not provided, this will add the fewest
        zeros necessary to make all identifiers the same length.
    """
    if index_len is None:
        # Avoid consuming a one-time-use generator.
        iterable = tuple(iterable)
        index_len = len(str(len(iterable)))

    def wrapper(func):
        setattr(func, DATA_ATTR, iterable)
        setattr(func, INDEX_LEN, index_len)
        return func

    return wrapper


def file_data(value, yaml_loader=None):
    """
    Method decorator to add to your test methods.

    Should be added to methods of instances of ``unittest.TestCase``.

    ``value`` should be a path relative to the directory of the file
    containing the decorated ``unittest.TestCase``. The file
    should contain JSON encoded data, that can either be a list or a
    dict.

    In case of a list, each value in the list will correspond to one
    test case, and the value will be concatenated to the test method
    name.

    In case of a dict, keys will be used as suffixes to the name of the
    test case, and values will be fed as test data.

    ``yaml_loader`` can be used to customize yaml deserialization.
    The default is ``None``, which results in using the ``yaml.safe_load``
    method.
    """
    def wrapper(func):
        setattr(func, FILE_ATTR, value)
        if yaml_loader:
            setattr(func, YAML_LOADER_ATTR, yaml_loader)
        return func
    return wrapper


def mk_test_name(name, value, index=0, index_len=5, name_fmt=TestNameFormat.DEFAULT):
    """
    Generate a new name for a test case.

    It will take the original test name and append an ordinal index and a
    string representation of the value, and convert the result into a valid
    python identifier by replacing extraneous characters with ``_``.

    We avoid doing str(value) if dealing with non-trivial values.
    The problem is possible different names with different runs, e.g.
    different order of dictionary keys (see PYTHONHASHSEED) or dealing
    with mock objects.
    Trivial scalar values are passed as is.

    A "trivial" value is a plain scalar, or a tuple or list consisting
    only of trivial values.

    The test name format is controlled by enum ``TestNameFormat`` as well. See
    the enum documentation for further details.
    """

    # Add zeros before index to keep order
    index = "{0:0{1}}".format(index + 1, index_len)
    if name_fmt is TestNameFormat.INDEX_ONLY or not is_trivial(value):
        return "{0}_{1}".format(name, index)
    try:
        value = str(value)
    except UnicodeEncodeError:
        # fallback for python2
        value = value.encode('ascii', 'backslashreplace')
    test_name = "{0}_{1}_{2}".format(name, index, value)
    return re.sub(r'\W|^(?=\d)', '_', test_name)


def feed_data(func, new_name, test_data_docstring, *args, **kwargs):
    """
    This internal method decorator feeds the test data item to the test.

    """
    @wraps(func)
    def wrapper(self):
        return func(self, *args, **kwargs)
    wrapper.__name__ = new_name
    wrapper.__wrapped__ = func
    # set docstring if exists
    if test_data_docstring is not None:
        wrapper.__doc__ = test_data_docstring
    else:
        # Try to call format on the docstring
        if func.__doc__:
            try:
                wrapper.__doc__ = func.__doc__.format(*args, **kwargs)
            except (IndexError, KeyError):
                # Maybe the user has added some of the formating strings
                # unintentionally in the docstring. Do not raise an exception
                # as it could be that user is not aware of the
                # formating feature.
                pass
    return wrapper


def add_test(cls, test_name, test_docstring, func, *args, **kwargs):
    """
    Add a test case to this class.

    The test will be based on an existing function but will give it a new
    name.

    """
    setattr(cls, test_name, feed_data(func, test_name, test_docstring,
            *args, **kwargs))


def process_file_data(cls, name, func, file_attr):
    """
    Process the parameter in the `file_data` decorator.
    """
    cls_path = os.path.abspath(inspect.getsourcefile(cls))
    data_file_path = os.path.join(os.path.dirname(cls_path), file_attr)

    def create_error_func(message):  # pylint: disable-msg=W0613
        def func(*args):
            raise ValueError(message % file_attr)
        return func

    # If file does not exist, provide an error function instead
    if not os.path.exists(data_file_path):
        test_name = mk_test_name(name, "error")
        test_docstring = """Error!"""
        add_test(cls, test_name, test_docstring,
                 create_error_func("%s does not exist"), None)
        return

    _is_yaml_file = data_file_path.endswith((".yml", ".yaml"))

    # Don't have YAML but want to use YAML file.
    if _is_yaml_file and not _have_yaml:
        test_name = mk_test_name(name, "error")
        test_docstring = """Error!"""
        add_test(
            cls,
            test_name,
            test_docstring,
            create_error_func("%s is a YAML file, please install PyYAML"),
            None
        )
        return

    with codecs.open(data_file_path, 'r', 'utf-8') as f:
        # Load the data from YAML or JSON
        if _is_yaml_file:
            if hasattr(func, YAML_LOADER_ATTR):
                yaml_loader = getattr(func, YAML_LOADER_ATTR)
                data = yaml.load(f, Loader=yaml_loader)
            else:
                data = yaml.safe_load(f)
        else:
            data = json.load(f)

    _add_tests_from_data(cls, name, func, data)


def _add_tests_from_data(cls, name, func, data):
    """
    Add tests from data loaded from the data file into the class
    """
    index_len = len(str(len(data)))
    for i, elem in enumerate(data):
        if isinstance(data, dict):
            key, value = elem, data[elem]
            test_name = mk_test_name(name, key, i, index_len)
        elif isinstance(data, list):
            value = elem
            test_name = mk_test_name(name, value, i, index_len)
        if isinstance(value, dict):
            add_test(cls, test_name, test_name, func, **value)
        else:
            add_test(cls, test_name, test_name, func, value)


def _is_primitive(obj):
    """Finds out if the obj is a "primitive". It is somewhat hacky but it works.
    """
    return not hasattr(obj, '__dict__')


def _get_test_data_docstring(func, value):
    """Returns a docstring based on the following resolution strategy:
    1. Passed value is not a "primitive" and has a docstring, then use it.
    2. In all other cases return None, i.e the test name is used.
    """
    if not _is_primitive(value) and value.__doc__:
        return value.__doc__
    else:
        return None


def ddt(arg=None, **kwargs):
    """
    Class decorator for subclasses of ``unittest.TestCase``.

    Apply this decorator to the test case class, and then
    decorate test methods with ``@data``.

    For each method decorated with ``@data``, this will effectively create as
    many methods as data items are passed as parameters to ``@data``.

    The names of the test methods follow the pattern
    ``original_test_name_{ordinal}_{data}``. ``ordinal`` is the position of the
    data argument, starting with 1.

    For data we use a string representation of the data value converted into a
    valid python identifier.  If ``data.__name__`` exists, we use that instead.

    For each method decorated with ``@file_data('test_data.json')``, the
    decorator will try to load the test_data.json file located relative
    to the python file containing the method that is decorated. It will,
    for each ``test_name`` key create as many methods in the list of values
    from the ``data`` key.

    Decorating with the keyword argument ``testNameFormat`` can control the
    format of the generated test names.  For example:

    - ``@ddt(testNameFormat=TestNameFormat.DEFAULT)`` will be index and values.

    - ``@ddt(testNameFormat=TestNameFormat.INDEX_ONLY)`` will be index only.

    - ``@ddt`` is the same as DEFAULT.

    """
    fmt_test_name = kwargs.get("testNameFormat", TestNameFormat.DEFAULT)

    def wrapper(cls):
        for name, func in list(cls.__dict__.items()):
            if hasattr(func, DATA_ATTR):
                index_len = getattr(func, INDEX_LEN)
                for i, v in enumerate(getattr(func, DATA_ATTR)):
                    test_name = mk_test_name(
                        name,
                        getattr(v, "__name__", v),
                        i,
                        index_len,
                        fmt_test_name
                    )
                    test_data_docstring = _get_test_data_docstring(func, v)
                    if hasattr(func, UNPACK_ATTR):
                        if isinstance(v, tuple) or isinstance(v, list):
                            add_test(
                                cls,
                                test_name,
                                test_data_docstring,
                                func,
                                *v
                            )
                        else:
                            # unpack dictionary
                            add_test(
                                cls,
                                test_name,
                                test_data_docstring,
                                func,
                                **v
                            )
                    else:
                        add_test(cls, test_name, test_data_docstring, func, v)
                delattr(cls, name)
            elif hasattr(func, FILE_ATTR):
                file_attr = getattr(func, FILE_ATTR)
                process_file_data(cls, name, func, file_attr)
                delattr(cls, name)
        return cls

    # ``arg`` is the unittest's test class when decorating with ``@ddt`` while
    # it is ``None`` when decorating a test class with ``@ddt(k=v)``.
    return wrapper(arg) if inspect.isclass(arg) else wrapper


def named_data(*named_values):
    """
    This decorator is to allow for meaningful names to be given to tests that would otherwise use @ddt.data and
    @ddt.unpack.

    Example of original ddt usage:
        @ddt.ddt
        class TestExample(TemplateTest):
            @ddt.data(
                [0, 1],
                [10, 11]
            )
            @ddt.unpack
            def test_values(self, value1, value2):
                ...

    Example of new usage:
        @ddt.ddt
        class TestExample(TemplateTest):
            @named_data(
                ['A', 0, 1],
                ['B', 10, 11],
            )
            def test_values(self, value1, value2):
                ...

    Note that @unpack is not used.

    :param list[Any] | dict[Any,Any] named_values: Each named_value should be a list with the name as the first
        argument, or a dictionary with 'name' as one of the keys. The name will be coerced to a string and all other
        values will be passed unchanged to the test.
    """
    type_of_first = None
    values = []
    for named_value in named_values:
        if type_of_first is None:
            type_of_first = type(named_value)

        if not isinstance(named_value, type_of_first):
            raise TypeError("@named_data expects all values to be of the same type.")

        if isinstance(named_value, list):
            value = NamedDataList(named_value[0], *named_value[1:])
            type_of_first = type_of_first or list

        elif isinstance(named_value, dict):
            if "name" not in named_value.keys():
                raise ValueError("@named_data expects a dictionary with a 'name' key.")
            value = NamedDataDict(**named_value)
            type_of_first = type_of_first or dict
        else:
            raise TypeError("@named_data expects a list or dictionary.")

        # Remove the __doc__ attribute so @ddt.data doesn't add the NamedData class docstrings to the test name.
        value.__doc__ = None

        values.append(value)

    def wrapper(func):
        data(*values)(unpack(func))
        return func

    return wrapper