add ydb deps

1 files changed, 996 insertions, 0 deletions

diff --git a/contrib/python/pytest/py3/_pytest/python_api.py b/contrib/python/pytest/py3/_pytest/python_api.py
new file mode 100644
index 0000000000..183356100c
--- /dev/null
+++ b/contrib/python/pytest/py3/_pytest/python_api.py
@@ -0,0 +1,996 @@
+import math
+import pprint
+from collections.abc import Collection
+from collections.abc import Sized
+from decimal import Decimal
+from numbers import Complex
+from types import TracebackType
+from typing import Any
+from typing import Callable
+from typing import cast
+from typing import ContextManager
+from typing import List
+from typing import Mapping
+from typing import Optional
+from typing import Pattern
+from typing import Sequence
+from typing import Tuple
+from typing import Type
+from typing import TYPE_CHECKING
+from typing import TypeVar
+from typing import Union
+
+if TYPE_CHECKING:
+    from numpy import ndarray
+
+
+import _pytest._code
+from _pytest.compat import final
+from _pytest.compat import STRING_TYPES
+from _pytest.compat import overload
+from _pytest.outcomes import fail
+
+
+def _non_numeric_type_error(value, at: Optional[str]) -> TypeError:
+    at_str = f" at {at}" if at else ""
+    return TypeError(
+        "cannot make approximate comparisons to non-numeric values: {!r} {}".format(
+            value, at_str
+        )
+    )
+
+
+def _compare_approx(
+    full_object: object,
+    message_data: Sequence[Tuple[str, str, str]],
+    number_of_elements: int,
+    different_ids: Sequence[object],
+    max_abs_diff: float,
+    max_rel_diff: float,
+) -> List[str]:
+    message_list = list(message_data)
+    message_list.insert(0, ("Index", "Obtained", "Expected"))
+    max_sizes = [0, 0, 0]
+    for index, obtained, expected in message_list:
+        max_sizes[0] = max(max_sizes[0], len(index))
+        max_sizes[1] = max(max_sizes[1], len(obtained))
+        max_sizes[2] = max(max_sizes[2], len(expected))
+    explanation = [
+        f"comparison failed. Mismatched elements: {len(different_ids)} / {number_of_elements}:",
+        f"Max absolute difference: {max_abs_diff}",
+        f"Max relative difference: {max_rel_diff}",
+    ] + [
+        f"{indexes:<{max_sizes[0]}} | {obtained:<{max_sizes[1]}} | {expected:<{max_sizes[2]}}"
+        for indexes, obtained, expected in message_list
+    ]
+    return explanation
+
+
+# builtin pytest.approx helper
+
+
+class ApproxBase:
+    """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: bool = False) -> None:
+        __tracebackhide__ = True
+        self.expected = expected
+        self.abs = abs
+        self.rel = rel
+        self.nan_ok = nan_ok
+        self._check_type()
+
+    def __repr__(self) -> str:
+        raise NotImplementedError
+
+    def _repr_compare(self, other_side: Any) -> List[str]:
+        return [
+            "comparison failed",
+            f"Obtained: {other_side}",
+            f"Expected: {self}",
+        ]
+
+    def __eq__(self, actual) -> bool:
+        return all(
+            a == self._approx_scalar(x) for a, x in self._yield_comparisons(actual)
+        )
+
+    def __bool__(self):
+        __tracebackhide__ = True
+        raise AssertionError(
+            "approx() is not supported in a boolean context.\nDid you mean: `assert a == approx(b)`?"
+        )
+
+    # Ignore type because of https://github.com/python/mypy/issues/4266.
+    __hash__ = None  # type: ignore
+
+    def __ne__(self, actual) -> bool:
+        return not (actual == self)
+
+    def _approx_scalar(self, x) -> "ApproxScalar":
+        if isinstance(x, Decimal):
+            return ApproxDecimal(x, rel=self.rel, abs=self.abs, nan_ok=self.nan_ok)
+        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) -> None:
+        """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.
+
+
+def _recursive_sequence_map(f, x):
+    """Recursively map a function over a sequence of arbitrary depth"""
+    if isinstance(x, (list, tuple)):
+        seq_type = type(x)
+        return seq_type(_recursive_sequence_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) -> str:
+        list_scalars = _recursive_sequence_map(
+            self._approx_scalar, self.expected.tolist()
+        )
+        return f"approx({list_scalars!r})"
+
+    def _repr_compare(self, other_side: "ndarray") -> List[str]:
+        import itertools
+        import math
+
+        def get_value_from_nested_list(
+            nested_list: List[Any], nd_index: Tuple[Any, ...]
+        ) -> Any:
+            """
+            Helper function to get the value out of a nested list, given an n-dimensional index.
+            This mimics numpy's indexing, but for raw nested python lists.
+            """
+            value: Any = nested_list
+            for i in nd_index:
+                value = value[i]
+            return value
+
+        np_array_shape = self.expected.shape
+        approx_side_as_seq = _recursive_sequence_map(
+            self._approx_scalar, self.expected.tolist()
+        )
+
+        if np_array_shape != other_side.shape:
+            return [
+                "Impossible to compare arrays with different shapes.",
+                f"Shapes: {np_array_shape} and {other_side.shape}",
+            ]
+
+        number_of_elements = self.expected.size
+        max_abs_diff = -math.inf
+        max_rel_diff = -math.inf
+        different_ids = []
+        for index in itertools.product(*(range(i) for i in np_array_shape)):
+            approx_value = get_value_from_nested_list(approx_side_as_seq, index)
+            other_value = get_value_from_nested_list(other_side, index)
+            if approx_value != other_value:
+                abs_diff = abs(approx_value.expected - other_value)
+                max_abs_diff = max(max_abs_diff, abs_diff)
+                if other_value == 0.0:
+                    max_rel_diff = math.inf
+                else:
+                    max_rel_diff = max(max_rel_diff, abs_diff / abs(other_value))
+                different_ids.append(index)
+
+        message_data = [
+            (
+                str(index),
+                str(get_value_from_nested_list(other_side, index)),
+                str(get_value_from_nested_list(approx_side_as_seq, index)),
+            )
+            for index in different_ids
+        ]
+        return _compare_approx(
+            self.expected,
+            message_data,
+            number_of_elements,
+            different_ids,
+            max_abs_diff,
+            max_rel_diff,
+        )
+
+    def __eq__(self, actual) -> bool:
+        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 Exception as e:
+                raise TypeError(f"cannot compare '{actual}' to numpy.ndarray") from e
+
+        if not np.isscalar(actual) and actual.shape != self.expected.shape:
+            return False
+
+        return super().__eq__(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) -> str:
+        return "approx({!r})".format(
+            {k: self._approx_scalar(v) for k, v in self.expected.items()}
+        )
+
+    def _repr_compare(self, other_side: Mapping[object, float]) -> List[str]:
+        import math
+
+        approx_side_as_map = {
+            k: self._approx_scalar(v) for k, v in self.expected.items()
+        }
+
+        number_of_elements = len(approx_side_as_map)
+        max_abs_diff = -math.inf
+        max_rel_diff = -math.inf
+        different_ids = []
+        for (approx_key, approx_value), other_value in zip(
+            approx_side_as_map.items(), other_side.values()
+        ):
+            if approx_value != other_value:
+                if approx_value.expected is not None and other_value is not None:
+                    max_abs_diff = max(
+                        max_abs_diff, abs(approx_value.expected - other_value)
+                    )
+                    if approx_value.expected == 0.0:
+                        max_rel_diff = math.inf
+                    else:
+                        max_rel_diff = max(
+                            max_rel_diff,
+                            abs(
+                                (approx_value.expected - other_value)
+                                / approx_value.expected
+                            ),
+                        )
+                different_ids.append(approx_key)
+
+        message_data = [
+            (str(key), str(other_side[key]), str(approx_side_as_map[key]))
+            for key in different_ids
+        ]
+
+        return _compare_approx(
+            self.expected,
+            message_data,
+            number_of_elements,
+            different_ids,
+            max_abs_diff,
+            max_rel_diff,
+        )
+
+    def __eq__(self, actual) -> bool:
+        try:
+            if set(actual.keys()) != set(self.expected.keys()):
+                return False
+        except AttributeError:
+            return False
+
+        return super().__eq__(actual)
+
+    def _yield_comparisons(self, actual):
+        for k in self.expected.keys():
+            yield actual[k], self.expected[k]
+
+    def _check_type(self) -> None:
+        __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)))
+
+
+class ApproxSequenceLike(ApproxBase):
+    """Perform approximate comparisons where the expected value is a sequence of numbers."""
+
+    def __repr__(self) -> str:
+        seq_type = type(self.expected)
+        if seq_type not in (tuple, list):
+            seq_type = list
+        return "approx({!r})".format(
+            seq_type(self._approx_scalar(x) for x in self.expected)
+        )
+
+    def _repr_compare(self, other_side: Sequence[float]) -> List[str]:
+        import math
+
+        if len(self.expected) != len(other_side):
+            return [
+                "Impossible to compare lists with different sizes.",
+                f"Lengths: {len(self.expected)} and {len(other_side)}",
+            ]
+
+        approx_side_as_map = _recursive_sequence_map(self._approx_scalar, self.expected)
+
+        number_of_elements = len(approx_side_as_map)
+        max_abs_diff = -math.inf
+        max_rel_diff = -math.inf
+        different_ids = []
+        for i, (approx_value, other_value) in enumerate(
+            zip(approx_side_as_map, other_side)
+        ):
+            if approx_value != other_value:
+                abs_diff = abs(approx_value.expected - other_value)
+                max_abs_diff = max(max_abs_diff, abs_diff)
+                if other_value == 0.0:
+                    max_rel_diff = math.inf
+                else:
+                    max_rel_diff = max(max_rel_diff, abs_diff / abs(other_value))
+                different_ids.append(i)
+
+        message_data = [
+            (str(i), str(other_side[i]), str(approx_side_as_map[i]))
+            for i in different_ids
+        ]
+
+        return _compare_approx(
+            self.expected,
+            message_data,
+            number_of_elements,
+            different_ids,
+            max_abs_diff,
+            max_rel_diff,
+        )
+
+    def __eq__(self, actual) -> bool:
+        try:
+            if len(actual) != len(self.expected):
+                return False
+        except TypeError:
+            return False
+        return super().__eq__(actual)
+
+    def _yield_comparisons(self, actual):
+        return zip(actual, self.expected)
+
+    def _check_type(self) -> None:
+        __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)))
+
+
+class ApproxScalar(ApproxBase):
+    """Perform approximate comparisons where the expected value is a single number."""
+
+    # Using Real should be better than this Union, but not possible yet:
+    # https://github.com/python/typeshed/pull/3108
+    DEFAULT_ABSOLUTE_TOLERANCE: Union[float, Decimal] = 1e-12
+    DEFAULT_RELATIVE_TOLERANCE: Union[float, Decimal] = 1e-6
+
+    def __repr__(self) -> str:
+        """Return a string communicating both the expected value and the
+        tolerance for the comparison being made.
+
+        For example, ``1.0 ± 1e-6``, ``(3+4j) ± 5e-6 ∠ ±180°``.
+        """
+        # Don't show a tolerance for values that aren't compared using
+        # tolerances, i.e. non-numerics and infinities. Need to call abs to
+        # handle complex numbers, e.g. (inf + 1j).
+        if (not isinstance(self.expected, (Complex, Decimal))) or math.isinf(
+            abs(self.expected)  # type: ignore[arg-type]
+        ):
+            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 = f"{self.tolerance:.1e}"
+            if (
+                isinstance(self.expected, Complex)
+                and self.expected.imag
+                and not math.isinf(self.tolerance)
+            ):
+                vetted_tolerance += " ∠ ±180°"
+        except ValueError:
+            vetted_tolerance = "???"
+
+        return f"{self.expected} ± {vetted_tolerance}"
+
+    def __eq__(self, actual) -> bool:
+        """Return whether the given value is equal to the expected value
+        within the pre-specified tolerance."""
+        asarray = _as_numpy_array(actual)
+        if asarray is not None:
+            # Call ``__eq__()`` manually to prevent infinite-recursion with
+            # numpy<1.13.  See #3748.
+            return all(self.__eq__(a) for a in asarray.flat)
+
+        # Short-circuit exact equality.
+        if actual == self.expected:
+            return True
+
+        # If either type is non-numeric, fall back to strict equality.
+        # NB: we need Complex, rather than just Number, to ensure that __abs__,
+        # __sub__, and __float__ are defined.
+        if not (
+            isinstance(self.expected, (Complex, Decimal))
+            and isinstance(actual, (Complex, Decimal))
+        ):
+            return False
+
+        # 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)):  # type: ignore[arg-type]
+            return self.nan_ok and math.isnan(abs(actual))  # type: ignore[arg-type]
+
+        # 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)):  # type: ignore[arg-type]
+            return False
+
+        # Return true if the two numbers are within the tolerance.
+        result: bool = abs(self.expected - actual) <= self.tolerance
+        return result
+
+    # Ignore type because of https://github.com/python/mypy/issues/4266.
+    __hash__ = None  # type: ignore
+
+    @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(
+                f"absolute tolerance can't be negative: {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(
+                f"relative tolerance can't be negative: {relative_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: bool = False) -> ApproxBase:
+    """Assert that two numbers (or two ordered sequences of numbers) are equal to each other
+    within some tolerance.
+
+    Due to the :doc:`python:tutorial/floatingpoint`, numbers that we
+    would intuitively expect to be equal are not always so::
+
+        >>> 0.1 + 0.2 == 0.3
+        False
+
+    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 ordered sequences of numbers::
+
+        >>> (0.1 + 0.2, 0.2 + 0.4) == approx((0.3, 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
+
+    Only ordered sequences are supported, because ``approx`` needs
+    to infer the relative position of the sequences without ambiguity. This means
+    ``sets`` and other unordered sequences are not supported.
+
+    Finally, dictionary *values* can also be compared::
+
+        >>> {'a': 0.1 + 0.2, 'b': 0.2 + 0.4} == approx({'a': 0.3, 'b': 0.6})
+        True
+
+    The comparison will be true if both mappings have the same keys and their
+    respective values match the expected tolerances.
+
+    **Tolerances**
+
+    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
+
+    You can also use ``approx`` to compare nonnumeric types, or dicts and
+    sequences containing nonnumeric types, in which case it falls back to
+    strict equality. This can be useful for comparing dicts and sequences that
+    can contain optional values::
+
+        >>> {"required": 1.0000005, "optional": None} == approx({"required": 1, "optional": None})
+        True
+        >>> [None, 1.0000005] == approx([None,1])
+        True
+        >>> ["foo", 1.0000005] == approx([None,1])
+        False
+
+    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.  More information: :py:func:`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 :py:func:`numpy.allclose`.  More information:
+      :std:doc:`numpy:reference/generated/numpy.isclose`.
+
+    - ``unittest.TestCase.assertAlmostEqual(a, b)``: True if ``a`` and ``b``
+      are within an absolute tolerance of ``1e-7``.  No relative tolerance is
+      considered , 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:
+      :py:meth:`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.
+
+    .. note::
+
+        ``approx`` can handle numpy arrays, but we recommend the
+        specialised test helpers in :std:doc:`numpy:reference/routines.testing`
+        if you need support for comparisons, NaNs, or ULP-based tolerances.
+
+        To match strings using regex, you can use
+        `Matches <https://github.com/asottile/re-assert#re_assertmatchespattern-str-args-kwargs>`_
+        from the
+        `re_assert package <https://github.com/asottile/re-assert>`_.
+
+    .. warning::
+
+       .. versionchanged:: 3.2
+
+       In order to avoid inconsistent behavior, :py:exc:`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: :py:meth:`object.__ge__`
+
+    .. versionchanged:: 3.7.1
+       ``approx`` raises ``TypeError`` when it encounters a dict value or
+       sequence element of nonnumeric type.
+
+    .. versionchanged:: 6.1.0
+       ``approx`` falls back to strict equality for nonnumeric types instead
+       of raising ``TypeError``.
+    """
+
+    # 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: Type[ApproxBase] = ApproxDecimal
+    elif isinstance(expected, Mapping):
+        cls = ApproxMapping
+    elif _is_numpy_array(expected):
+        expected = _as_numpy_array(expected)
+        cls = ApproxNumpy
+    elif (
+        hasattr(expected, "__getitem__")
+        and isinstance(expected, Sized)
+        # Type ignored because the error is wrong -- not unreachable.
+        and not isinstance(expected, STRING_TYPES)  # type: ignore[unreachable]
+    ):
+        cls = ApproxSequenceLike
+    elif (
+        isinstance(expected, Collection)
+        # Type ignored because the error is wrong -- not unreachable.
+        and not isinstance(expected, STRING_TYPES)  # type: ignore[unreachable]
+    ):
+        msg = f"pytest.approx() only supports ordered sequences, but got: {repr(expected)}"
+        raise TypeError(msg)
+    else:
+        cls = ApproxScalar
+
+    return cls(expected, rel, abs, nan_ok)
+
+
+def _is_numpy_array(obj: object) -> bool:
+    """
+    Return true if the given object is implicitly convertible to ndarray,
+    and numpy is already imported.
+    """
+    return _as_numpy_array(obj) is not None
+
+
+def _as_numpy_array(obj: object) -> Optional["ndarray"]:
+    """
+    Return an ndarray if the given object is implicitly convertible to ndarray,
+    and numpy is already imported, otherwise None.
+    """
+    import sys
+
+    np: Any = sys.modules.get("numpy")
+    if np is not None:
+        # avoid infinite recursion on numpy scalars, which have __array__
+        if np.isscalar(obj):
+            return None
+        elif isinstance(obj, np.ndarray):
+            return obj
+        elif hasattr(obj, "__array__") or hasattr("obj", "__array_interface__"):
+            return np.asarray(obj)
+    return None
+
+
+# builtin pytest.raises helper
+
+E = TypeVar("E", bound=BaseException)
+
+
+@overload
+def raises(
+    expected_exception: Union[Type[E], Tuple[Type[E], ...]],
+    *,
+    match: Optional[Union[str, Pattern[str]]] = ...,
+) -> "RaisesContext[E]":
+    ...
+
+
+@overload
+def raises(  # noqa: F811
+    expected_exception: Union[Type[E], Tuple[Type[E], ...]],
+    func: Callable[..., Any],
+    *args: Any,
+    **kwargs: Any,
+) -> _pytest._code.ExceptionInfo[E]:
+    ...
+
+
+def raises(  # noqa: F811
+    expected_exception: Union[Type[E], Tuple[Type[E], ...]], *args: Any, **kwargs: Any
+) -> Union["RaisesContext[E]", _pytest._code.ExceptionInfo[E]]:
+    r"""Assert that a code block/function call raises an exception.
+
+    :param typing.Type[E] | typing.Tuple[typing.Type[E], ...] expected_exception:
+        The expected exception type, or a tuple if one of multiple possible
+        exception types are expected.
+    :kwparam str | typing.Pattern[str] | None 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 :func:`re.search`.
+
+        To match a literal string that may contain :ref:`special characters
+        <re-syntax>`, the pattern can first be escaped with :func:`re.escape`.
+
+        (This is only used when :py:func:`pytest.raises` is used as a context manager,
+        and passed through to the function otherwise.
+        When using :py:func:`pytest.raises` as a function, you can use:
+        ``pytest.raises(Exc, func, match="passed on").match("my pattern")``.)
+
+    .. currentmodule:: _pytest._code
+
+    Use ``pytest.raises`` as a context manager, which will capture the exception of the given
+    type::
+
+        >>> import pytest
+        >>> with pytest.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 pytest.raises(ValueError, match='must be 0 or None'):
+        ...     raise ValueError("value must be 0 or None")
+
+        >>> with pytest.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 pytest.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"
+
+    .. 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 pytest.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 pytest.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.
+        More detailed information can be found in the official Python
+        documentation for :ref:`the try statement <python:try>`.
+    """
+    __tracebackhide__ = True
+
+    if not expected_exception:
+        raise ValueError(
+            f"Expected an exception type or a tuple of exception types, but got `{expected_exception!r}`. "
+            f"Raising exceptions is already understood as failing the test, so you don't need "
+            f"any special code to say 'this should never raise an exception'."
+        )
+    if isinstance(expected_exception, type):
+        expected_exceptions: Tuple[Type[E], ...] = (expected_exception,)
+    else:
+        expected_exceptions = expected_exception
+    for exc in expected_exceptions:
+        if not isinstance(exc, type) or not issubclass(exc, BaseException):
+            msg = "expected exception must be a BaseException type, not {}"  # type: ignore[unreachable]
+            not_a = exc.__name__ if isinstance(exc, type) else type(exc).__name__
+            raise TypeError(msg.format(not_a))
+
+    message = f"DID NOT RAISE {expected_exception}"
+
+    if not args:
+        match: Optional[Union[str, Pattern[str]]] = kwargs.pop("match", None)
+        if kwargs:
+            msg = "Unexpected keyword arguments passed to pytest.raises: "
+            msg += ", ".join(sorted(kwargs))
+            msg += "\nUse context-manager form instead?"
+            raise TypeError(msg)
+        return RaisesContext(expected_exception, message, match)
+    else:
+        func = args[0]
+        if not callable(func):
+            raise TypeError(f"{func!r} object (type: {type(func)}) must be callable")
+        try:
+            func(*args[1:], **kwargs)
+        except expected_exception as e:
+            return _pytest._code.ExceptionInfo.from_exception(e)
+    fail(message)
+
+
+# This doesn't work with mypy for now. Use fail.Exception instead.
+raises.Exception = fail.Exception  # type: ignore
+
+
+@final
+class RaisesContext(ContextManager[_pytest._code.ExceptionInfo[E]]):
+    def __init__(
+        self,
+        expected_exception: Union[Type[E], Tuple[Type[E], ...]],
+        message: str,
+        match_expr: Optional[Union[str, Pattern[str]]] = None,
+    ) -> None:
+        self.expected_exception = expected_exception
+        self.message = message
+        self.match_expr = match_expr
+        self.excinfo: Optional[_pytest._code.ExceptionInfo[E]] = None
+
+    def __enter__(self) -> _pytest._code.ExceptionInfo[E]:
+        self.excinfo = _pytest._code.ExceptionInfo.for_later()
+        return self.excinfo
+
+    def __exit__(
+        self,
+        exc_type: Optional[Type[BaseException]],
+        exc_val: Optional[BaseException],
+        exc_tb: Optional[TracebackType],
+    ) -> bool:
+        __tracebackhide__ = True
+        if exc_type is None:
+            fail(self.message)
+        assert self.excinfo is not None
+        if not issubclass(exc_type, self.expected_exception):
+            return False
+        # Cast to narrow the exception type now that it's verified.
+        exc_info = cast(Tuple[Type[E], E, TracebackType], (exc_type, exc_val, exc_tb))
+        self.excinfo.fill_unfilled(exc_info)
+        if self.match_expr is not None:
+            self.excinfo.match(self.match_expr)
+        return True