mirror of
https://github.com/thegeeklab/ansible-later.git
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737 lines
27 KiB
Python
737 lines
27 KiB
Python
from __future__ import absolute_import
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import math
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import pprint
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import sys
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import warnings
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from decimal import Decimal
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from numbers import Number
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import six
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from more_itertools.more import always_iterable
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from six.moves import filterfalse
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from six.moves import zip
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import _pytest._code
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from _pytest import deprecated
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from _pytest.compat import isclass
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from _pytest.compat import Iterable
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from _pytest.compat import Mapping
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from _pytest.compat import Sized
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from _pytest.compat import STRING_TYPES
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from _pytest.outcomes import fail
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BASE_TYPE = (type, STRING_TYPES)
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def _cmp_raises_type_error(self, other):
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"""__cmp__ implementation which raises TypeError. Used
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by Approx base classes to implement only == and != and raise a
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TypeError for other comparisons.
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Needed in Python 2 only, Python 3 all it takes is not implementing the
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other operators at all.
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"""
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__tracebackhide__ = True
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raise TypeError(
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"Comparison operators other than == and != not supported by approx objects"
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)
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def _non_numeric_type_error(value, at):
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at_str = " at {}".format(at) if at else ""
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return TypeError(
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"cannot make approximate comparisons to non-numeric values: {!r} {}".format(
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value, at_str
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)
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)
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# builtin pytest.approx helper
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class ApproxBase(object):
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"""
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Provide shared utilities for making approximate comparisons between numbers
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or sequences of numbers.
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"""
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# Tell numpy to use our `__eq__` operator instead of its.
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__array_ufunc__ = None
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__array_priority__ = 100
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def __init__(self, expected, rel=None, abs=None, nan_ok=False):
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__tracebackhide__ = True
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self.expected = expected
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self.abs = abs
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self.rel = rel
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self.nan_ok = nan_ok
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self._check_type()
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def __repr__(self):
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raise NotImplementedError
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def __eq__(self, actual):
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return all(
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a == self._approx_scalar(x) for a, x in self._yield_comparisons(actual)
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)
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__hash__ = None
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def __ne__(self, actual):
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return not (actual == self)
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if sys.version_info[0] == 2:
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__cmp__ = _cmp_raises_type_error
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def _approx_scalar(self, x):
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return ApproxScalar(x, rel=self.rel, abs=self.abs, nan_ok=self.nan_ok)
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def _yield_comparisons(self, actual):
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"""
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Yield all the pairs of numbers to be compared. This is used to
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implement the `__eq__` method.
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"""
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raise NotImplementedError
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def _check_type(self):
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"""
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Raise a TypeError if the expected value is not a valid type.
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"""
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# This is only a concern if the expected value is a sequence. In every
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# other case, the approx() function ensures that the expected value has
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# a numeric type. For this reason, the default is to do nothing. The
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# classes that deal with sequences should reimplement this method to
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# raise if there are any non-numeric elements in the sequence.
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pass
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def _recursive_list_map(f, x):
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if isinstance(x, list):
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return list(_recursive_list_map(f, xi) for xi in x)
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else:
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return f(x)
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class ApproxNumpy(ApproxBase):
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"""
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Perform approximate comparisons where the expected value is numpy array.
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"""
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def __repr__(self):
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list_scalars = _recursive_list_map(self._approx_scalar, self.expected.tolist())
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return "approx({!r})".format(list_scalars)
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if sys.version_info[0] == 2:
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__cmp__ = _cmp_raises_type_error
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def __eq__(self, actual):
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import numpy as np
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# self.expected is supposed to always be an array here
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if not np.isscalar(actual):
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try:
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actual = np.asarray(actual)
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except: # noqa
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raise TypeError("cannot compare '{}' to numpy.ndarray".format(actual))
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if not np.isscalar(actual) and actual.shape != self.expected.shape:
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return False
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return ApproxBase.__eq__(self, actual)
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def _yield_comparisons(self, actual):
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import numpy as np
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# `actual` can either be a numpy array or a scalar, it is treated in
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# `__eq__` before being passed to `ApproxBase.__eq__`, which is the
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# only method that calls this one.
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if np.isscalar(actual):
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for i in np.ndindex(self.expected.shape):
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yield actual, self.expected[i].item()
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else:
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for i in np.ndindex(self.expected.shape):
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yield actual[i].item(), self.expected[i].item()
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class ApproxMapping(ApproxBase):
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"""
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Perform approximate comparisons where the expected value is a mapping with
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numeric values (the keys can be anything).
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"""
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def __repr__(self):
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return "approx({!r})".format(
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{k: self._approx_scalar(v) for k, v in self.expected.items()}
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)
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def __eq__(self, actual):
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if set(actual.keys()) != set(self.expected.keys()):
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return False
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return ApproxBase.__eq__(self, actual)
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def _yield_comparisons(self, actual):
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for k in self.expected.keys():
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yield actual[k], self.expected[k]
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def _check_type(self):
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__tracebackhide__ = True
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for key, value in self.expected.items():
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if isinstance(value, type(self.expected)):
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msg = "pytest.approx() does not support nested dictionaries: key={!r} value={!r}\n full mapping={}"
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raise TypeError(msg.format(key, value, pprint.pformat(self.expected)))
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elif not isinstance(value, Number):
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raise _non_numeric_type_error(self.expected, at="key={!r}".format(key))
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class ApproxSequencelike(ApproxBase):
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"""
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Perform approximate comparisons where the expected value is a sequence of
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numbers.
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"""
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def __repr__(self):
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seq_type = type(self.expected)
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if seq_type not in (tuple, list, set):
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seq_type = list
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return "approx({!r})".format(
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seq_type(self._approx_scalar(x) for x in self.expected)
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)
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def __eq__(self, actual):
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if len(actual) != len(self.expected):
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return False
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return ApproxBase.__eq__(self, actual)
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def _yield_comparisons(self, actual):
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return zip(actual, self.expected)
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def _check_type(self):
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__tracebackhide__ = True
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for index, x in enumerate(self.expected):
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if isinstance(x, type(self.expected)):
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msg = "pytest.approx() does not support nested data structures: {!r} at index {}\n full sequence: {}"
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raise TypeError(msg.format(x, index, pprint.pformat(self.expected)))
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elif not isinstance(x, Number):
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raise _non_numeric_type_error(
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self.expected, at="index {}".format(index)
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)
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class ApproxScalar(ApproxBase):
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"""
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Perform approximate comparisons where the expected value is a single number.
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"""
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DEFAULT_ABSOLUTE_TOLERANCE = 1e-12
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DEFAULT_RELATIVE_TOLERANCE = 1e-6
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def __repr__(self):
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"""
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Return a string communicating both the expected value and the tolerance
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for the comparison being made, e.g. '1.0 +- 1e-6'. Use the unicode
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plus/minus symbol if this is python3 (it's too hard to get right for
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python2).
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"""
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if isinstance(self.expected, complex):
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return str(self.expected)
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# Infinities aren't compared using tolerances, so don't show a
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# tolerance.
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if math.isinf(self.expected):
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return str(self.expected)
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# If a sensible tolerance can't be calculated, self.tolerance will
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# raise a ValueError. In this case, display '???'.
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try:
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vetted_tolerance = "{:.1e}".format(self.tolerance)
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except ValueError:
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vetted_tolerance = "???"
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if sys.version_info[0] == 2:
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return "{} +- {}".format(self.expected, vetted_tolerance)
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else:
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return u"{} \u00b1 {}".format(self.expected, vetted_tolerance)
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def __eq__(self, actual):
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"""
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Return true if the given value is equal to the expected value within
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the pre-specified tolerance.
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"""
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if _is_numpy_array(actual):
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# Call ``__eq__()`` manually to prevent infinite-recursion with
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# numpy<1.13. See #3748.
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return all(self.__eq__(a) for a in actual.flat)
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# Short-circuit exact equality.
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if actual == self.expected:
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return True
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# Allow the user to control whether NaNs are considered equal to each
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# other or not. The abs() calls are for compatibility with complex
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# numbers.
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if math.isnan(abs(self.expected)):
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return self.nan_ok and math.isnan(abs(actual))
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# Infinity shouldn't be approximately equal to anything but itself, but
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# if there's a relative tolerance, it will be infinite and infinity
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# will seem approximately equal to everything. The equal-to-itself
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# case would have been short circuited above, so here we can just
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# return false if the expected value is infinite. The abs() call is
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# for compatibility with complex numbers.
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if math.isinf(abs(self.expected)):
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return False
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# Return true if the two numbers are within the tolerance.
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return abs(self.expected - actual) <= self.tolerance
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__hash__ = None
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@property
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def tolerance(self):
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"""
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Return the tolerance for the comparison. This could be either an
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absolute tolerance or a relative tolerance, depending on what the user
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specified or which would be larger.
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"""
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def set_default(x, default):
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return x if x is not None else default
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# Figure out what the absolute tolerance should be. ``self.abs`` is
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# either None or a value specified by the user.
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absolute_tolerance = set_default(self.abs, self.DEFAULT_ABSOLUTE_TOLERANCE)
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if absolute_tolerance < 0:
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raise ValueError(
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"absolute tolerance can't be negative: {}".format(absolute_tolerance)
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)
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if math.isnan(absolute_tolerance):
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raise ValueError("absolute tolerance can't be NaN.")
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# If the user specified an absolute tolerance but not a relative one,
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# just return the absolute tolerance.
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if self.rel is None:
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if self.abs is not None:
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return absolute_tolerance
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# Figure out what the relative tolerance should be. ``self.rel`` is
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# either None or a value specified by the user. This is done after
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# we've made sure the user didn't ask for an absolute tolerance only,
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# because we don't want to raise errors about the relative tolerance if
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# we aren't even going to use it.
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relative_tolerance = set_default(
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self.rel, self.DEFAULT_RELATIVE_TOLERANCE
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) * abs(self.expected)
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if relative_tolerance < 0:
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raise ValueError(
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"relative tolerance can't be negative: {}".format(absolute_tolerance)
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)
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if math.isnan(relative_tolerance):
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raise ValueError("relative tolerance can't be NaN.")
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# Return the larger of the relative and absolute tolerances.
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return max(relative_tolerance, absolute_tolerance)
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class ApproxDecimal(ApproxScalar):
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"""
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Perform approximate comparisons where the expected value is a decimal.
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"""
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DEFAULT_ABSOLUTE_TOLERANCE = Decimal("1e-12")
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DEFAULT_RELATIVE_TOLERANCE = Decimal("1e-6")
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def approx(expected, rel=None, abs=None, nan_ok=False):
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"""
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Assert that two numbers (or two sets of numbers) are equal to each other
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within some tolerance.
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Due to the `intricacies of floating-point arithmetic`__, numbers that we
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would intuitively expect to be equal are not always so::
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>>> 0.1 + 0.2 == 0.3
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False
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__ https://docs.python.org/3/tutorial/floatingpoint.html
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This problem is commonly encountered when writing tests, e.g. when making
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sure that floating-point values are what you expect them to be. One way to
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deal with this problem is to assert that two floating-point numbers are
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equal to within some appropriate tolerance::
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>>> abs((0.1 + 0.2) - 0.3) < 1e-6
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True
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However, comparisons like this are tedious to write and difficult to
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understand. Furthermore, absolute comparisons like the one above are
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usually discouraged because there's no tolerance that works well for all
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situations. ``1e-6`` is good for numbers around ``1``, but too small for
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very big numbers and too big for very small ones. It's better to express
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the tolerance as a fraction of the expected value, but relative comparisons
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like that are even more difficult to write correctly and concisely.
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The ``approx`` class performs floating-point comparisons using a syntax
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that's as intuitive as possible::
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>>> from pytest import approx
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>>> 0.1 + 0.2 == approx(0.3)
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True
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The same syntax also works for sequences of numbers::
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>>> (0.1 + 0.2, 0.2 + 0.4) == approx((0.3, 0.6))
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True
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Dictionary *values*::
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>>> {'a': 0.1 + 0.2, 'b': 0.2 + 0.4} == approx({'a': 0.3, 'b': 0.6})
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True
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``numpy`` arrays::
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>>> import numpy as np # doctest: +SKIP
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>>> np.array([0.1, 0.2]) + np.array([0.2, 0.4]) == approx(np.array([0.3, 0.6])) # doctest: +SKIP
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True
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And for a ``numpy`` array against a scalar::
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>>> import numpy as np # doctest: +SKIP
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>>> np.array([0.1, 0.2]) + np.array([0.2, 0.1]) == approx(0.3) # doctest: +SKIP
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True
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By default, ``approx`` considers numbers within a relative tolerance of
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``1e-6`` (i.e. one part in a million) of its expected value to be equal.
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This treatment would lead to surprising results if the expected value was
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``0.0``, because nothing but ``0.0`` itself is relatively close to ``0.0``.
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To handle this case less surprisingly, ``approx`` also considers numbers
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within an absolute tolerance of ``1e-12`` of its expected value to be
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equal. Infinity and NaN are special cases. Infinity is only considered
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equal to itself, regardless of the relative tolerance. NaN is not
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considered equal to anything by default, but you can make it be equal to
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itself by setting the ``nan_ok`` argument to True. (This is meant to
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facilitate comparing arrays that use NaN to mean "no data".)
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Both the relative and absolute tolerances can be changed by passing
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arguments to the ``approx`` constructor::
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>>> 1.0001 == approx(1)
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False
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>>> 1.0001 == approx(1, rel=1e-3)
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True
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>>> 1.0001 == approx(1, abs=1e-3)
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True
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If you specify ``abs`` but not ``rel``, the comparison will not consider
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the relative tolerance at all. In other words, two numbers that are within
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the default relative tolerance of ``1e-6`` will still be considered unequal
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if they exceed the specified absolute tolerance. If you specify both
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``abs`` and ``rel``, the numbers will be considered equal if either
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tolerance is met::
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>>> 1 + 1e-8 == approx(1)
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True
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>>> 1 + 1e-8 == approx(1, abs=1e-12)
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False
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>>> 1 + 1e-8 == approx(1, rel=1e-6, abs=1e-12)
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True
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If you're thinking about using ``approx``, then you might want to know how
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it compares to other good ways of comparing floating-point numbers. All of
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these algorithms are based on relative and absolute tolerances and should
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agree for the most part, but they do have meaningful differences:
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- ``math.isclose(a, b, rel_tol=1e-9, abs_tol=0.0)``: True if the relative
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tolerance is met w.r.t. either ``a`` or ``b`` or if the absolute
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tolerance is met. Because the relative tolerance is calculated w.r.t.
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both ``a`` and ``b``, this test is symmetric (i.e. neither ``a`` nor
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``b`` is a "reference value"). You have to specify an absolute tolerance
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if you want to compare to ``0.0`` because there is no tolerance by
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default. Only available in python>=3.5. `More information...`__
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__ https://docs.python.org/3/library/math.html#math.isclose
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- ``numpy.isclose(a, b, rtol=1e-5, atol=1e-8)``: True if the difference
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between ``a`` and ``b`` is less that the sum of the relative tolerance
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w.r.t. ``b`` and the absolute tolerance. Because the relative tolerance
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is only calculated w.r.t. ``b``, this test is asymmetric and you can
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think of ``b`` as the reference value. Support for comparing sequences
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is provided by ``numpy.allclose``. `More information...`__
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__ http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.isclose.html
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- ``unittest.TestCase.assertAlmostEqual(a, b)``: True if ``a`` and ``b``
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are within an absolute tolerance of ``1e-7``. No relative tolerance is
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considered and the absolute tolerance cannot be changed, so this function
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is not appropriate for very large or very small numbers. Also, it's only
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available in subclasses of ``unittest.TestCase`` and it's ugly because it
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doesn't follow PEP8. `More information...`__
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__ https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertAlmostEqual
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- ``a == pytest.approx(b, rel=1e-6, abs=1e-12)``: True if the relative
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tolerance is met w.r.t. ``b`` or if the absolute tolerance is met.
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Because the relative tolerance is only calculated w.r.t. ``b``, this test
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is asymmetric and you can think of ``b`` as the reference value. In the
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special case that you explicitly specify an absolute tolerance but not a
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relative tolerance, only the absolute tolerance is considered.
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.. warning::
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.. versionchanged:: 3.2
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In order to avoid inconsistent behavior, ``TypeError`` is
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raised for ``>``, ``>=``, ``<`` and ``<=`` comparisons.
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The example below illustrates the problem::
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assert approx(0.1) > 0.1 + 1e-10 # calls approx(0.1).__gt__(0.1 + 1e-10)
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assert 0.1 + 1e-10 > approx(0.1) # calls approx(0.1).__lt__(0.1 + 1e-10)
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In the second example one expects ``approx(0.1).__le__(0.1 + 1e-10)``
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to be called. But instead, ``approx(0.1).__lt__(0.1 + 1e-10)`` is used to
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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, asserts that the exception matches a text or regex
|
|
|
|
:kwparam message: **(deprecated since 4.1)** if specified, provides a custom failure message
|
|
if the exception is not raised
|
|
|
|
.. 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.
|
|
|
|
.. 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(kwargs.keys())
|
|
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)
|
|
six.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
|