# Source code for _pytest.python_api

    import math
    import sys

    import py
    from six.moves import zip

    from _pytest.compat import isclass
    from _pytest.outcomes import fail
    import _pytest._code

    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')

    # builtin pytest.approx helper

    class ApproxBase(object):
        """
        Provide shared utilities for making approximate comparisons between numbers
        or sequences of numbers.
        """

        def __init__(self, expected, rel=None, abs=None, nan_ok=False):
            self.expected = expected
            self.abs = abs
            self.rel = rel
            self.nan_ok = nan_ok

        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

    class ApproxNumpy(ApproxBase):
        """
        Perform approximate comparisons for numpy arrays.
        """

        # Tell numpy to use our `__eq__` operator instead of its.
        __array_priority__ = 100

        def __repr__(self):
            # It might be nice to rewrite this function to account for the
            # shape of the array...
            return "approx({0!r})".format(list(
                self._approx_scalar(x) for x in self.expected))

        if sys.version_info[0] == 2:
            __cmp__ = _cmp_raises_type_error

        def __eq__(self, actual):
            import numpy as np

            try:
                actual = np.asarray(actual)
            except:  # noqa
                raise TypeError("cannot compare '{0}' to numpy.ndarray".format(actual))

            if actual.shape != self.expected.shape:
                return False

            return ApproxBase.__eq__(self, actual)

        def _yield_comparisons(self, actual):
            import numpy as np

            # We can be sure that `actual` is a numpy array, because it's
            # casted in `__eq__` before being passed to `ApproxBase.__eq__`,
            # which is the only method that calls this one.
            for i in np.ndindex(self.expected.shape):
                yield actual[i], self.expected[i]

    class ApproxMapping(ApproxBase):
        """
        Perform approximate comparisons for mappings where the values are numbers
        (the keys can be anything).
        """

        def __repr__(self):
            return "approx({0!r})".format(dict(
                (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]

    class ApproxSequence(ApproxBase):
        """
        Perform approximate comparisons for sequences of numbers.
        """

        # Tell numpy to use our `__eq__` operator instead of its.
        __array_priority__ = 100

        def __repr__(self):
            seq_type = type(self.expected)
            if seq_type not in (tuple, list, set):
                seq_type = list
            return "approx({0!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)

    class ApproxScalar(ApproxBase):
        """
        Perform approximate comparisons for single numbers only.
        """

        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 '{0} +- {1}'.format(self.expected, vetted_tolerance)
            else:
                return u'{0} \u00b1 {1}'.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.
            """

            # 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, 1e-12)

            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, 1e-6) * 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)

    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

        And ``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

        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__
        """

        from collections import Mapping, Sequence
        from _pytest.compat import STRING_TYPES as String

        # 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).
        #
        # This architecture is really driven by the need to support numpy arrays.
        # The only way to override `==` for arrays without requiring that approx be
        # the left operand is to inherit the approx object from `numpy.ndarray`.
        # But that can't be a general solution, because it requires (1) numpy to be
        # installed and (2) the expected value to be a numpy array.  So the general
        # solution is to delegate each type of expected value to a different class.
        #
        # This has the advantage that it made it easy to support mapping types
        # (i.e. dict).  The old code accepted mapping types, but would only compare
        # their keys, which is probably not what most people would expect.

        if _is_numpy_array(expected):
            cls = ApproxNumpy
        elif isinstance(expected, Mapping):
            cls = ApproxMapping
        elif isinstance(expected, Sequence) and not isinstance(expected, String):
            cls = ApproxSequence
        else:
            cls = ApproxScalar

        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 inspect

        for cls in inspect.getmro(type(obj)):
            if cls.__module__ == 'numpy':
                try:
                    import numpy as np
                    return isinstance(obj, np.ndarray)
                except ImportError:
                    pass

        return False

    # builtin pytest.raises helper

    def raises(expected_exception, *args, **kwargs):
        """
        Assert that a code block/function call raises ``expected_exception``
        and raise a failure exception otherwise.

        This helper produces a ``ExceptionInfo()`` object (see below).

        You may use this function as a context manager::

            >>> with raises(ZeroDivisionError):
            ...    1/0

        .. versionchanged:: 2.10

        In the context manager form you may use the keyword argument
        ``message`` to specify a custom failure message::

            >>> with raises(ZeroDivisionError, message="Expecting ZeroDivisionError"):
            ...    pass
            Traceback (most recent call last):
              ...
            Failed: Expecting ZeroDivisionError

        .. 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 == 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 == ValueError

        Since version ``3.1`` you can 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")

        **Legacy forms**

        The forms below are fully supported but are discouraged for new code because the
        context manager form is regarded as more readable and less error-prone.

        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 ...>

        It is also possible to pass a string to be evaluated at runtime::

            >>> raises(ZeroDivisionError, "f(0)")
            <ExceptionInfo ...>

        The string will be evaluated using the same ``locals()`` and ``globals()``
        at the moment of the ``raises`` call.

        .. autoclass:: _pytest._code.ExceptionInfo
            :members:

        .. 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
        msg = ("exceptions must be old-style classes or"
               " derived from BaseException, not %s")
        if isinstance(expected_exception, tuple):
            for exc in expected_exception:
                if not isclass(exc):
                    raise TypeError(msg % type(exc))
        elif not isclass(expected_exception):
            raise TypeError(msg % type(expected_exception))

        message = "DID NOT RAISE {0}".format(expected_exception)
        match_expr = None

        if not args:
            if "message" in kwargs:
                message = kwargs.pop("message")
            if "match" in kwargs:
                match_expr = kwargs.pop("match")
                message += " matching '{0}'".format(match_expr)
            return RaisesContext(expected_exception, message, match_expr)
        elif isinstance(args[0], str):
            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()
                py.builtin.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()
        else:
            func = args[0]
            try:
                func(*args[1:], **kwargs)
            except expected_exception:
                return _pytest._code.ExceptionInfo()
        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 = object.__new__(_pytest._code.ExceptionInfo)
            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:
                self.excinfo.match(self.match_expr)
            return suppress_exception