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@@ -0,0 +1,743 @@
+# -*- coding: utf-8 -*-
+from __future__ import absolute_import
+
+import math
+import pprint
+import sys
+import warnings
+from decimal import Decimal
+from numbers import Number
+
+from more_itertools.more import always_iterable
+from six.moves import filterfalse
+from six.moves import zip
+
+import _pytest._code
+from _pytest import deprecated
+from _pytest.compat import isclass
+from _pytest.compat import Iterable
+from _pytest.compat import Mapping
+from _pytest.compat import Sized
+from _pytest.compat import STRING_TYPES
+from _pytest.outcomes import fail
+
+BASE_TYPE = (type, STRING_TYPES)
+
+
+def _cmp_raises_type_error(self, other):
+    """__cmp__ implementation which raises TypeError. Used
+    by Approx base classes to implement only == and != and raise a
+    TypeError for other comparisons.
+
+    Needed in Python 2 only, Python 3 all it takes is not implementing the
+    other operators at all.
+    """
+    __tracebackhide__ = True
+    raise TypeError(
+        "Comparison operators other than == and != not supported by approx objects"
+    )
+
+
+def _non_numeric_type_error(value, at):
+    at_str = " at {}".format(at) if at else ""
+    return TypeError(
+        "cannot make approximate comparisons to non-numeric values: {!r} {}".format(
+            value, at_str
+        )
+    )
+
+
+# builtin pytest.approx helper
+
+
+class ApproxBase(object):
+    """
+    Provide shared utilities for making approximate comparisons between numbers
+    or sequences of numbers.
+    """
+
+    # Tell numpy to use our `__eq__` operator instead of its.
+    __array_ufunc__ = None
+    __array_priority__ = 100
+
+    def __init__(self, expected, rel=None, abs=None, nan_ok=False):
+        __tracebackhide__ = True
+        self.expected = expected
+        self.abs = abs
+        self.rel = rel
+        self.nan_ok = nan_ok
+        self._check_type()
+
+    def __repr__(self):
+        raise NotImplementedError
+
+    def __eq__(self, actual):
+        return all(
+            a == self._approx_scalar(x) for a, x in self._yield_comparisons(actual)
+        )
+
+    __hash__ = None
+
+    def __ne__(self, actual):
+        return not (actual == self)
+
+    if sys.version_info[0] == 2:
+        __cmp__ = _cmp_raises_type_error
+
+    def _approx_scalar(self, x):
+        return ApproxScalar(x, rel=self.rel, abs=self.abs, nan_ok=self.nan_ok)
+
+    def _yield_comparisons(self, actual):
+        """
+        Yield all the pairs of numbers to be compared.  This is used to
+        implement the `__eq__` method.
+        """
+        raise NotImplementedError
+
+    def _check_type(self):
+        """
+        Raise a TypeError if the expected value is not a valid type.
+        """
+        # This is only a concern if the expected value is a sequence.  In every
+        # other case, the approx() function ensures that the expected value has
+        # a numeric type.  For this reason, the default is to do nothing.  The
+        # classes that deal with sequences should reimplement this method to
+        # raise if there are any non-numeric elements in the sequence.
+        pass
+
+
+def _recursive_list_map(f, x):
+    if isinstance(x, list):
+        return list(_recursive_list_map(f, xi) for xi in x)
+    else:
+        return f(x)
+
+
+class ApproxNumpy(ApproxBase):
+    """
+    Perform approximate comparisons where the expected value is numpy array.
+    """
+
+    def __repr__(self):
+        list_scalars = _recursive_list_map(self._approx_scalar, self.expected.tolist())
+        return "approx({!r})".format(list_scalars)
+
+    if sys.version_info[0] == 2:
+        __cmp__ = _cmp_raises_type_error
+
+    def __eq__(self, actual):
+        import numpy as np
+
+        # self.expected is supposed to always be an array here
+
+        if not np.isscalar(actual):
+            try:
+                actual = np.asarray(actual)
+            except:  # noqa
+                raise TypeError("cannot compare '{}' to numpy.ndarray".format(actual))
+
+        if not np.isscalar(actual) and actual.shape != self.expected.shape:
+            return False
+
+        return ApproxBase.__eq__(self, actual)
+
+    def _yield_comparisons(self, actual):
+        import numpy as np
+
+        # `actual` can either be a numpy array or a scalar, it is treated in
+        # `__eq__` before being passed to `ApproxBase.__eq__`, which is the
+        # only method that calls this one.
+
+        if np.isscalar(actual):
+            for i in np.ndindex(self.expected.shape):
+                yield actual, self.expected[i].item()
+        else:
+            for i in np.ndindex(self.expected.shape):
+                yield actual[i].item(), self.expected[i].item()
+
+
+class ApproxMapping(ApproxBase):
+    """
+    Perform approximate comparisons where the expected value is a mapping with
+    numeric values (the keys can be anything).
+    """
+
+    def __repr__(self):
+        return "approx({!r})".format(
+            {k: self._approx_scalar(v) for k, v in self.expected.items()}
+        )
+
+    def __eq__(self, actual):
+        if set(actual.keys()) != set(self.expected.keys()):
+            return False
+
+        return ApproxBase.__eq__(self, actual)
+
+    def _yield_comparisons(self, actual):
+        for k in self.expected.keys():
+            yield actual[k], self.expected[k]
+
+    def _check_type(self):
+        __tracebackhide__ = True
+        for key, value in self.expected.items():
+            if isinstance(value, type(self.expected)):
+                msg = "pytest.approx() does not support nested dictionaries: key={!r} value={!r}\n  full mapping={}"
+                raise TypeError(msg.format(key, value, pprint.pformat(self.expected)))
+            elif not isinstance(value, Number):
+                raise _non_numeric_type_error(self.expected, at="key={!r}".format(key))
+
+
+class ApproxSequencelike(ApproxBase):
+    """
+    Perform approximate comparisons where the expected value is a sequence of
+    numbers.
+    """
+
+    def __repr__(self):
+        seq_type = type(self.expected)
+        if seq_type not in (tuple, list, set):
+            seq_type = list
+        return "approx({!r})".format(
+            seq_type(self._approx_scalar(x) for x in self.expected)
+        )
+
+    def __eq__(self, actual):
+        if len(actual) != len(self.expected):
+            return False
+        return ApproxBase.__eq__(self, actual)
+
+    def _yield_comparisons(self, actual):
+        return zip(actual, self.expected)
+
+    def _check_type(self):
+        __tracebackhide__ = True
+        for index, x in enumerate(self.expected):
+            if isinstance(x, type(self.expected)):
+                msg = "pytest.approx() does not support nested data structures: {!r} at index {}\n  full sequence: {}"
+                raise TypeError(msg.format(x, index, pprint.pformat(self.expected)))
+            elif not isinstance(x, Number):
+                raise _non_numeric_type_error(
+                    self.expected, at="index {}".format(index)
+                )
+
+
+class ApproxScalar(ApproxBase):
+    """
+    Perform approximate comparisons where the expected value is a single number.
+    """
+
+    DEFAULT_ABSOLUTE_TOLERANCE = 1e-12
+    DEFAULT_RELATIVE_TOLERANCE = 1e-6
+
+    def __repr__(self):
+        """
+        Return a string communicating both the expected value and the tolerance
+        for the comparison being made, e.g. '1.0 +- 1e-6'.  Use the unicode
+        plus/minus symbol if this is python3 (it's too hard to get right for
+        python2).
+        """
+        if isinstance(self.expected, complex):
+            return str(self.expected)
+
+        # Infinities aren't compared using tolerances, so don't show a
+        # tolerance.
+        if math.isinf(self.expected):
+            return str(self.expected)
+
+        # If a sensible tolerance can't be calculated, self.tolerance will
+        # raise a ValueError.  In this case, display '???'.
+        try:
+            vetted_tolerance = "{:.1e}".format(self.tolerance)
+        except ValueError:
+            vetted_tolerance = "???"
+
+        if sys.version_info[0] == 2:
+            return "{} +- {}".format(self.expected, vetted_tolerance)
+        else:
+            return u"{} \u00b1 {}".format(self.expected, vetted_tolerance)
+
+    def __eq__(self, actual):
+        """
+        Return true if the given value is equal to the expected value within
+        the pre-specified tolerance.
+        """
+        if _is_numpy_array(actual):
+            # Call ``__eq__()`` manually to prevent infinite-recursion with
+            # numpy<1.13.  See #3748.
+            return all(self.__eq__(a) for a in actual.flat)
+
+        # Short-circuit exact equality.
+        if actual == self.expected:
+            return True
+
+        # Allow the user to control whether NaNs are considered equal to each
+        # other or not.  The abs() calls are for compatibility with complex
+        # numbers.
+        if math.isnan(abs(self.expected)):
+            return self.nan_ok and math.isnan(abs(actual))
+
+        # Infinity shouldn't be approximately equal to anything but itself, but
+        # if there's a relative tolerance, it will be infinite and infinity
+        # will seem approximately equal to everything.  The equal-to-itself
+        # case would have been short circuited above, so here we can just
+        # return false if the expected value is infinite.  The abs() call is
+        # for compatibility with complex numbers.
+        if math.isinf(abs(self.expected)):
+            return False
+
+        # Return true if the two numbers are within the tolerance.
+        return abs(self.expected - actual) <= self.tolerance
+
+    __hash__ = None
+
+    @property
+    def tolerance(self):
+        """
+        Return the tolerance for the comparison.  This could be either an
+        absolute tolerance or a relative tolerance, depending on what the user
+        specified or which would be larger.
+        """
+
+        def set_default(x, default):
+            return x if x is not None else default
+
+        # Figure out what the absolute tolerance should be.  ``self.abs`` is
+        # either None or a value specified by the user.
+        absolute_tolerance = set_default(self.abs, self.DEFAULT_ABSOLUTE_TOLERANCE)
+
+        if absolute_tolerance < 0:
+            raise ValueError(
+                "absolute tolerance can't be negative: {}".format(absolute_tolerance)
+            )
+        if math.isnan(absolute_tolerance):
+            raise ValueError("absolute tolerance can't be NaN.")
+
+        # If the user specified an absolute tolerance but not a relative one,
+        # just return the absolute tolerance.
+        if self.rel is None:
+            if self.abs is not None:
+                return absolute_tolerance
+
+        # Figure out what the relative tolerance should be.  ``self.rel`` is
+        # either None or a value specified by the user.  This is done after
+        # we've made sure the user didn't ask for an absolute tolerance only,
+        # because we don't want to raise errors about the relative tolerance if
+        # we aren't even going to use it.
+        relative_tolerance = set_default(
+            self.rel, self.DEFAULT_RELATIVE_TOLERANCE
+        ) * abs(self.expected)
+
+        if relative_tolerance < 0:
+            raise ValueError(
+                "relative tolerance can't be negative: {}".format(absolute_tolerance)
+            )
+        if math.isnan(relative_tolerance):
+            raise ValueError("relative tolerance can't be NaN.")
+
+        # Return the larger of the relative and absolute tolerances.
+        return max(relative_tolerance, absolute_tolerance)
+
+
+class ApproxDecimal(ApproxScalar):
+    """
+    Perform approximate comparisons where the expected value is a decimal.
+    """
+
+    DEFAULT_ABSOLUTE_TOLERANCE = Decimal("1e-12")
+    DEFAULT_RELATIVE_TOLERANCE = Decimal("1e-6")
+
+
+def approx(expected, rel=None, abs=None, nan_ok=False):
+    """
+    Assert that two numbers (or two sets of numbers) are equal to each other
+    within some tolerance.
+
+    Due to the `intricacies of floating-point arithmetic`__, numbers that we
+    would intuitively expect to be equal are not always so::
+
+        >>> 0.1 + 0.2 == 0.3
+        False
+
+    __ https://docs.python.org/3/tutorial/floatingpoint.html
+
+    This problem is commonly encountered when writing tests, e.g. when making
+    sure that floating-point values are what you expect them to be.  One way to
+    deal with this problem is to assert that two floating-point numbers are
+    equal to within some appropriate tolerance::
+
+        >>> abs((0.1 + 0.2) - 0.3) < 1e-6
+        True
+
+    However, comparisons like this are tedious to write and difficult to
+    understand.  Furthermore, absolute comparisons like the one above are
+    usually discouraged because there's no tolerance that works well for all
+    situations.  ``1e-6`` is good for numbers around ``1``, but too small for
+    very big numbers and too big for very small ones.  It's better to express
+    the tolerance as a fraction of the expected value, but relative comparisons
+    like that are even more difficult to write correctly and concisely.
+
+    The ``approx`` class performs floating-point comparisons using a syntax
+    that's as intuitive as possible::
+
+        >>> from pytest import approx
+        >>> 0.1 + 0.2 == approx(0.3)
+        True
+
+    The same syntax also works for sequences of numbers::
+
+        >>> (0.1 + 0.2, 0.2 + 0.4) == approx((0.3, 0.6))
+        True
+
+    Dictionary *values*::
+
+        >>> {'a': 0.1 + 0.2, 'b': 0.2 + 0.4} == approx({'a': 0.3, 'b': 0.6})
+        True
+
+    ``numpy`` arrays::
+
+        >>> import numpy as np                                                          # doctest: +SKIP
+        >>> np.array([0.1, 0.2]) + np.array([0.2, 0.4]) == approx(np.array([0.3, 0.6])) # doctest: +SKIP
+        True
+
+    And for a ``numpy`` array against a scalar::
+
+        >>> import numpy as np                                         # doctest: +SKIP
+        >>> np.array([0.1, 0.2]) + np.array([0.2, 0.1]) == approx(0.3) # doctest: +SKIP
+        True
+
+    By default, ``approx`` considers numbers within a relative tolerance of
+    ``1e-6`` (i.e. one part in a million) of its expected value to be equal.
+    This treatment would lead to surprising results if the expected value was
+    ``0.0``, because nothing but ``0.0`` itself is relatively close to ``0.0``.
+    To handle this case less surprisingly, ``approx`` also considers numbers
+    within an absolute tolerance of ``1e-12`` of its expected value to be
+    equal.  Infinity and NaN are special cases.  Infinity is only considered
+    equal to itself, regardless of the relative tolerance.  NaN is not
+    considered equal to anything by default, but you can make it be equal to
+    itself by setting the ``nan_ok`` argument to True.  (This is meant to
+    facilitate comparing arrays that use NaN to mean "no data".)
+
+    Both the relative and absolute tolerances can be changed by passing
+    arguments to the ``approx`` constructor::
+
+        >>> 1.0001 == approx(1)
+        False
+        >>> 1.0001 == approx(1, rel=1e-3)
+        True
+        >>> 1.0001 == approx(1, abs=1e-3)
+        True
+
+    If you specify ``abs`` but not ``rel``, the comparison will not consider
+    the relative tolerance at all.  In other words, two numbers that are within
+    the default relative tolerance of ``1e-6`` will still be considered unequal
+    if they exceed the specified absolute tolerance.  If you specify both
+    ``abs`` and ``rel``, the numbers will be considered equal if either
+    tolerance is met::
+
+        >>> 1 + 1e-8 == approx(1)
+        True
+        >>> 1 + 1e-8 == approx(1, abs=1e-12)
+        False
+        >>> 1 + 1e-8 == approx(1, rel=1e-6, abs=1e-12)
+        True
+
+    If you're thinking about using ``approx``, then you might want to know how
+    it compares to other good ways of comparing floating-point numbers.  All of
+    these algorithms are based on relative and absolute tolerances and should
+    agree for the most part, but they do have meaningful differences:
+
+    - ``math.isclose(a, b, rel_tol=1e-9, abs_tol=0.0)``:  True if the relative
+      tolerance is met w.r.t. either ``a`` or ``b`` or if the absolute
+      tolerance is met.  Because the relative tolerance is calculated w.r.t.
+      both ``a`` and ``b``, this test is symmetric (i.e.  neither ``a`` nor
+      ``b`` is a "reference value").  You have to specify an absolute tolerance
+      if you want to compare to ``0.0`` because there is no tolerance by
+      default.  Only available in python>=3.5.  `More information...`__
+
+      __ https://docs.python.org/3/library/math.html#math.isclose
+
+    - ``numpy.isclose(a, b, rtol=1e-5, atol=1e-8)``: True if the difference
+      between ``a`` and ``b`` is less that the sum of the relative tolerance
+      w.r.t. ``b`` and the absolute tolerance.  Because the relative tolerance
+      is only calculated w.r.t. ``b``, this test is asymmetric and you can
+      think of ``b`` as the reference value.  Support for comparing sequences
+      is provided by ``numpy.allclose``.  `More information...`__
+
+      __ http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.isclose.html
+
+    - ``unittest.TestCase.assertAlmostEqual(a, b)``: True if ``a`` and ``b``
+      are within an absolute tolerance of ``1e-7``.  No relative tolerance is
+      considered and the absolute tolerance cannot be changed, so this function
+      is not appropriate for very large or very small numbers.  Also, it's only
+      available in subclasses of ``unittest.TestCase`` and it's ugly because it
+      doesn't follow PEP8.  `More information...`__
+
+      __ https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertAlmostEqual
+
+    - ``a == pytest.approx(b, rel=1e-6, abs=1e-12)``: True if the relative
+      tolerance is met w.r.t. ``b`` or if the absolute tolerance is met.
+      Because the relative tolerance is only calculated w.r.t. ``b``, this test
+      is asymmetric and you can think of ``b`` as the reference value.  In the
+      special case that you explicitly specify an absolute tolerance but not a
+      relative tolerance, only the absolute tolerance is considered.
+
+    .. warning::
+
+       .. versionchanged:: 3.2
+
+       In order to avoid inconsistent behavior, ``TypeError`` is
+       raised for ``>``, ``>=``, ``<`` and ``<=`` comparisons.
+       The example below illustrates the problem::
+
+           assert approx(0.1) > 0.1 + 1e-10  # calls approx(0.1).__gt__(0.1 + 1e-10)
+           assert 0.1 + 1e-10 > approx(0.1)  # calls approx(0.1).__lt__(0.1 + 1e-10)
+
+       In the second example one expects ``approx(0.1).__le__(0.1 + 1e-10)``
+       to be called. But instead, ``approx(0.1).__lt__(0.1 + 1e-10)`` is used to
+       comparison. This is because the call hierarchy of rich comparisons
+       follows a fixed behavior. `More information...`__
+
+       __ https://docs.python.org/3/reference/datamodel.html#object.__ge__
+    """
+
+    # Delegate the comparison to a class that knows how to deal with the type
+    # of the expected value (e.g. int, float, list, dict, numpy.array, etc).
+    #
+    # The primary responsibility of these classes is to implement ``__eq__()``
+    # and ``__repr__()``.  The former is used to actually check if some
+    # "actual" value is equivalent to the given expected value within the
+    # allowed tolerance.  The latter is used to show the user the expected
+    # value and tolerance, in the case that a test failed.
+    #
+    # The actual logic for making approximate comparisons can be found in
+    # ApproxScalar, which is used to compare individual numbers.  All of the
+    # other Approx classes eventually delegate to this class.  The ApproxBase
+    # class provides some convenient methods and overloads, but isn't really
+    # essential.
+
+    __tracebackhide__ = True
+
+    if isinstance(expected, Decimal):
+        cls = ApproxDecimal
+    elif isinstance(expected, Number):
+        cls = ApproxScalar
+    elif isinstance(expected, Mapping):
+        cls = ApproxMapping
+    elif _is_numpy_array(expected):
+        cls = ApproxNumpy
+    elif (
+        isinstance(expected, Iterable)
+        and isinstance(expected, Sized)
+        and not isinstance(expected, STRING_TYPES)
+    ):
+        cls = ApproxSequencelike
+    else:
+        raise _non_numeric_type_error(expected, at=None)
+
+    return cls(expected, rel, abs, nan_ok)
+
+
+def _is_numpy_array(obj):
+    """
+    Return true if the given object is a numpy array.  Make a special effort to
+    avoid importing numpy unless it's really necessary.
+    """
+    import sys
+
+    np = sys.modules.get("numpy")
+    if np is not None:
+        return isinstance(obj, np.ndarray)
+    return False
+
+
+# builtin pytest.raises helper
+
+
+def raises(expected_exception, *args, **kwargs):
+    r"""
+    Assert that a code block/function call raises ``expected_exception``
+    or raise a failure exception otherwise.
+
+    :kwparam match: if specified, a string containing a regular expression,
+        or a regular expression object, that is tested against the string
+        representation of the exception using ``re.search``. To match a literal
+        string that may contain `special characters`__, the pattern can
+        first be escaped with ``re.escape``.
+
+    __ https://docs.python.org/3/library/re.html#regular-expression-syntax
+
+    :kwparam message: **(deprecated since 4.1)** if specified, provides a custom failure message
+        if the exception is not raised. See :ref:`the deprecation docs <raises message deprecated>` for a workaround.
+
+    .. currentmodule:: _pytest._code
+
+    Use ``pytest.raises`` as a context manager, which will capture the exception of the given
+    type::
+
+        >>> with raises(ZeroDivisionError):
+        ...    1/0
+
+    If the code block does not raise the expected exception (``ZeroDivisionError`` in the example
+    above), or no exception at all, the check will fail instead.
+
+    You can also use the keyword argument ``match`` to assert that the
+    exception matches a text or regex::
+
+        >>> with raises(ValueError, match='must be 0 or None'):
+        ...     raise ValueError("value must be 0 or None")
+
+        >>> with raises(ValueError, match=r'must be \d+$'):
+        ...     raise ValueError("value must be 42")
+
+    The context manager produces an :class:`ExceptionInfo` object which can be used to inspect the
+    details of the captured exception::
+
+        >>> with raises(ValueError) as exc_info:
+        ...     raise ValueError("value must be 42")
+        >>> assert exc_info.type is ValueError
+        >>> assert exc_info.value.args[0] == "value must be 42"
+
+    .. deprecated:: 4.1
+
+        In the context manager form you may use the keyword argument
+        ``message`` to specify a custom failure message that will be displayed
+        in case the ``pytest.raises`` check fails. This has been deprecated as it
+        is considered error prone as users often mean to use ``match`` instead.
+        See :ref:`the deprecation docs <raises message deprecated>` for a workaround.
+
+    .. note::
+
+       When using ``pytest.raises`` as a context manager, it's worthwhile to
+       note that normal context manager rules apply and that the exception
+       raised *must* be the final line in the scope of the context manager.
+       Lines of code after that, within the scope of the context manager will
+       not be executed. For example::
+
+           >>> value = 15
+           >>> with raises(ValueError) as exc_info:
+           ...     if value > 10:
+           ...         raise ValueError("value must be <= 10")
+           ...     assert exc_info.type is ValueError  # this will not execute
+
+       Instead, the following approach must be taken (note the difference in
+       scope)::
+
+           >>> with raises(ValueError) as exc_info:
+           ...     if value > 10:
+           ...         raise ValueError("value must be <= 10")
+           ...
+           >>> assert exc_info.type is ValueError
+
+    **Using with** ``pytest.mark.parametrize``
+
+    When using :ref:`pytest.mark.parametrize ref`
+    it is possible to parametrize tests such that
+    some runs raise an exception and others do not.
+
+    See :ref:`parametrizing_conditional_raising` for an example.
+
+    **Legacy form**
+
+    It is possible to specify a callable by passing a to-be-called lambda::
+
+        >>> raises(ZeroDivisionError, lambda: 1/0)
+        <ExceptionInfo ...>
+
+    or you can specify an arbitrary callable with arguments::
+
+        >>> def f(x): return 1/x
+        ...
+        >>> raises(ZeroDivisionError, f, 0)
+        <ExceptionInfo ...>
+        >>> raises(ZeroDivisionError, f, x=0)
+        <ExceptionInfo ...>
+
+    The form above is fully supported but discouraged for new code because the
+    context manager form is regarded as more readable and less error-prone.
+
+    .. note::
+        Similar to caught exception objects in Python, explicitly clearing
+        local references to returned ``ExceptionInfo`` objects can
+        help the Python interpreter speed up its garbage collection.
+
+        Clearing those references breaks a reference cycle
+        (``ExceptionInfo`` --> caught exception --> frame stack raising
+        the exception --> current frame stack --> local variables -->
+        ``ExceptionInfo``) which makes Python keep all objects referenced
+        from that cycle (including all local variables in the current
+        frame) alive until the next cyclic garbage collection run. See the
+        official Python ``try`` statement documentation for more detailed
+        information.
+
+    """
+    __tracebackhide__ = True
+    for exc in filterfalse(isclass, always_iterable(expected_exception, BASE_TYPE)):
+        msg = (
+            "exceptions must be old-style classes or"
+            " derived from BaseException, not %s"
+        )
+        raise TypeError(msg % type(exc))
+
+    message = "DID NOT RAISE {}".format(expected_exception)
+    match_expr = None
+
+    if not args:
+        if "message" in kwargs:
+            message = kwargs.pop("message")
+            warnings.warn(deprecated.RAISES_MESSAGE_PARAMETER, stacklevel=2)
+        if "match" in kwargs:
+            match_expr = kwargs.pop("match")
+        if kwargs:
+            msg = "Unexpected keyword arguments passed to pytest.raises: "
+            msg += ", ".join(sorted(kwargs))
+            raise TypeError(msg)
+        return RaisesContext(expected_exception, message, match_expr)
+    elif isinstance(args[0], str):
+        warnings.warn(deprecated.RAISES_EXEC, stacklevel=2)
+        (code,) = args
+        assert isinstance(code, str)
+        frame = sys._getframe(1)
+        loc = frame.f_locals.copy()
+        loc.update(kwargs)
+        # print "raises frame scope: %r" % frame.f_locals
+        try:
+            code = _pytest._code.Source(code).compile(_genframe=frame)
+            exec(code, frame.f_globals, loc)
+            # XXX didn't mean f_globals == f_locals something special?
+            #     this is destroyed here ...
+        except expected_exception:
+            return _pytest._code.ExceptionInfo.from_current()
+    else:
+        func = args[0]
+        try:
+            func(*args[1:], **kwargs)
+        except expected_exception:
+            return _pytest._code.ExceptionInfo.from_current()
+    fail(message)
+
+
+raises.Exception = fail.Exception
+
+
+class RaisesContext(object):
+    def __init__(self, expected_exception, message, match_expr):
+        self.expected_exception = expected_exception
+        self.message = message
+        self.match_expr = match_expr
+        self.excinfo = None
+
+    def __enter__(self):
+        self.excinfo = _pytest._code.ExceptionInfo.for_later()
+        return self.excinfo
+
+    def __exit__(self, *tp):
+        __tracebackhide__ = True
+        if tp[0] is None:
+            fail(self.message)
+        self.excinfo.__init__(tp)
+        suppress_exception = issubclass(self.excinfo.type, self.expected_exception)
+        if sys.version_info[0] == 2 and suppress_exception:
+            sys.exc_clear()
+        if self.match_expr is not None and suppress_exception:
+            self.excinfo.match(self.match_expr)
+        return suppress_exception