Bump tempora from 5.6.0 to 5.7.0 (#2371)

* Bump tempora from 5.6.0 to 5.7.0

Bumps [tempora](https://github.com/jaraco/tempora) from 5.6.0 to 5.7.0.
- [Release notes](https://github.com/jaraco/tempora/releases)
- [Changelog](https://github.com/jaraco/tempora/blob/main/NEWS.rst)
- [Commits](https://github.com/jaraco/tempora/compare/v5.6.0...v5.7.0)

---
updated-dependencies:
- dependency-name: tempora
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

* Update tempora==5.7.0

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: JonnyWong16 <9099342+JonnyWong16@users.noreply.github.com>

[skip ci]

lib/jaraco/functools/__init__.py

@@ -14,6 +14,14 @@ def compose(*funcs):
     """
     Compose any number of unary functions into a single unary function.
 
+    Comparable to
+    `function composition <https://en.wikipedia.org/wiki/Function_composition>`_
+    in mathematics:
+
+    ``h = g ∘ f`` implies ``h(x) = g(f(x))``.
+
+    In Python, ``h = compose(g, f)``.
+
     >>> import textwrap
     >>> expected = str.strip(textwrap.dedent(compose.__doc__))
     >>> strip_and_dedent = compose(str.strip, textwrap.dedent)

lib/more_itertools/__init__.py

@@ -3,4 +3,4 @@
 from .more import *  # noqa
 from .recipes import *  # noqa
 
-__version__ = '10.3.0'
+__version__ = '10.4.0'

lib/more_itertools/more.py

@@ -3,8 +3,9 @@ import warnings
 
 from collections import Counter, defaultdict, deque, abc
 from collections.abc import Sequence
+from contextlib import suppress
 from functools import cached_property, partial, reduce, wraps
-from heapq import heapify, heapreplace, heappop
+from heapq import heapify, heapreplace
 from itertools import (
     chain,
     combinations,
@@ -21,10 +22,10 @@ from itertools import (
     zip_longest,
     product,
 )
-from math import comb, e, exp, factorial, floor, fsum, log, perm, tau
+from math import comb, e, exp, factorial, floor, fsum, log, log1p, perm, tau
 from queue import Empty, Queue
-from random import random, randrange, uniform
+from random import random, randrange, shuffle, uniform
-from operator import itemgetter, mul, sub, gt, lt, ge, le
+from operator import itemgetter, mul, sub, gt, lt, le
 from sys import hexversion, maxsize
 from time import monotonic
 
@@ -34,7 +35,6 @@ from .recipes import (
     UnequalIterablesError,
     consume,
     flatten,
-    pairwise,
     powerset,
     take,
     unique_everseen,
@@ -473,12 +473,10 @@ def ilen(iterable):
     This consumes the iterable, so handle with care.
 
     """
-    # This approach was selected because benchmarks showed it's likely the
+    # This is the "most beautiful of the fast variants" of this function.
-    # fastest of the known implementations at the time of writing.
+    # If you think you can improve on it, please ensure that your version
-    # See GitHub tracker: #236, #230.
+    # is both 10x faster and 10x more beautiful.
-    counter = count()
+    return sum(compress(repeat(1), zip(iterable)))
-    deque(zip(iterable, counter), maxlen=0)
-    return next(counter)
 
 
 def iterate(func, start):
@@ -666,9 +664,9 @@ def distinct_permutations(iterable, r=None):
         >>> sorted(distinct_permutations([1, 0, 1]))
         [(0, 1, 1), (1, 0, 1), (1, 1, 0)]
 
-    Equivalent to ``set(permutations(iterable))``, except duplicates are not
+    Equivalent to yielding from ``set(permutations(iterable))``, except
-    generated and thrown away. For larger input sequences this is much more
+    duplicates are not generated and thrown away. For larger input sequences
-    efficient.
+    this is much more efficient.
 
     Duplicate permutations arise when there are duplicated elements in the
     input iterable. The number of items returned is
@@ -683,6 +681,25 @@ def distinct_permutations(iterable, r=None):
         >>> sorted(distinct_permutations(range(3), r=2))
         [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
 
+    *iterable* need not be sortable, but note that using equal (``x == y``)
+    but non-identical (``id(x) != id(y)``) elements may produce surprising
+    behavior. For example, ``1`` and ``True`` are equal but non-identical:
+
+        >>> list(distinct_permutations([1, True, '3']))  # doctest: +SKIP
+        [
+            (1, True, '3'),
+            (1, '3', True),
+            ('3', 1, True)
+        ]
+        >>> list(distinct_permutations([1, 2, '3']))  # doctest: +SKIP
+        [
+            (1, 2, '3'),
+            (1, '3', 2),
+            (2, 1, '3'),
+            (2, '3', 1),
+            ('3', 1, 2),
+            ('3', 2, 1)
+        ]
     """
 
     # Algorithm: https://w.wiki/Qai
@@ -749,14 +766,44 @@ def distinct_permutations(iterable, r=None):
             i += 1
             head[i:], tail[:] = tail[: r - i], tail[r - i :]
 
-    items = sorted(iterable)
+    items = list(iterable)
+
+    try:
+        items.sort()
+        sortable = True
+    except TypeError:
+        sortable = False
+
+        indices_dict = defaultdict(list)
+
+        for item in items:
+            indices_dict[items.index(item)].append(item)
+
+        indices = [items.index(item) for item in items]
+        indices.sort()
+
+        equivalent_items = {k: cycle(v) for k, v in indices_dict.items()}
+
+        def permuted_items(permuted_indices):
+            return tuple(
+                next(equivalent_items[index]) for index in permuted_indices
+            )
 
     size = len(items)
     if r is None:
         r = size
 
+    # functools.partial(_partial, ... )
+    algorithm = _full if (r == size) else partial(_partial, r=r)
+
     if 0 < r <= size:
-        return _full(items) if (r == size) else _partial(items, r)
+        if sortable:
+            return algorithm(items)
+        else:
+            return (
+                permuted_items(permuted_indices)
+                for permuted_indices in algorithm(indices)
+            )
 
     return iter(() if r else ((),))
 
@@ -1743,7 +1790,9 @@ def zip_offset(*iterables, offsets, longest=False, fillvalue=None):
     return zip(*staggered)
 
 
-def sort_together(iterables, key_list=(0,), key=None, reverse=False):
+def sort_together(
+    iterables, key_list=(0,), key=None, reverse=False, strict=False
+):
     """Return the input iterables sorted together, with *key_list* as the
     priority for sorting. All iterables are trimmed to the length of the
     shortest one.
@@ -1782,6 +1831,10 @@ def sort_together(iterables, key_list=(0,), key=None, reverse=False):
         >>> sort_together([(1, 2, 3), ('c', 'b', 'a')], reverse=True)
         [(3, 2, 1), ('a', 'b', 'c')]
 
+    If the *strict* keyword argument is ``True``, then
+    ``UnequalIterablesError`` will be raised if any of the iterables have
+    different lengths.
+
     """
     if key is None:
         # if there is no key function, the key argument to sorted is an
@@ -1804,8 +1857,9 @@ def sort_together(iterables, key_list=(0,), key=None, reverse=False):
                 *get_key_items(zipped_items)
             )
 
+    zipper = zip_equal if strict else zip
     return list(
-        zip(*sorted(zip(*iterables), key=key_argument, reverse=reverse))
+        zipper(*sorted(zipper(*iterables), key=key_argument, reverse=reverse))
     )
 
 
@@ -2747,8 +2801,6 @@ class seekable:
         >>> it.seek(0)
         >>> next(it), next(it), next(it)
         ('0', '1', '2')
-        >>> next(it)
-        '3'
 
     You can also seek forward:
 
@@ -2756,15 +2808,29 @@ class seekable:
         >>> it.seek(10)
         >>> next(it)
         '10'
-        >>> it.relative_seek(-2)  # Seeking relative to the current position
-        >>> next(it)
-        '9'
         >>> it.seek(20)  # Seeking past the end of the source isn't a problem
         >>> list(it)
         []
         >>> it.seek(0)  # Resetting works even after hitting the end
+        >>> next(it)
+        '0'
+
+    Call :meth:`relative_seek` to seek relative to the source iterator's
+    current position.
+
+        >>> it = seekable((str(n) for n in range(20)))
         >>> next(it), next(it), next(it)
         ('0', '1', '2')
+        >>> it.relative_seek(2)
+        >>> next(it)
+        '5'
+        >>> it.relative_seek(-3)  # Source is at '6', we move back to '3'
+        >>> next(it)
+        '3'
+        >>> it.relative_seek(-3)  # Source is at '4', we move back to '1'
+        >>> next(it)
+        '1'
+
 
     Call :meth:`peek` to look ahead one item without advancing the iterator:
 
@@ -2873,8 +2939,10 @@ class seekable:
             consume(self, remainder)
 
     def relative_seek(self, count):
-        index = len(self._cache)
+        if self._index is None:
-        self.seek(max(index + count, 0))
+            self._index = len(self._cache)
+
+        self.seek(max(self._index + count, 0))
 
 
 class run_length:
@@ -2903,7 +2971,7 @@ class run_length:
 
     @staticmethod
     def decode(iterable):
-        return chain.from_iterable(repeat(k, n) for k, n in iterable)
+        return chain.from_iterable(starmap(repeat, iterable))
 
 
 def exactly_n(iterable, n, predicate=bool):
@@ -2924,14 +2992,34 @@ def exactly_n(iterable, n, predicate=bool):
     return len(take(n + 1, filter(predicate, iterable))) == n
 
 
-def circular_shifts(iterable):
+def circular_shifts(iterable, steps=1):
-    """Return a list of circular shifts of *iterable*.
+    """Yield the circular shifts of *iterable*.
 
-    >>> circular_shifts(range(4))
+    >>> list(circular_shifts(range(4)))
     [(0, 1, 2, 3), (1, 2, 3, 0), (2, 3, 0, 1), (3, 0, 1, 2)]
+
+    Set *steps* to the number of places to rotate to the left
+    (or to the right if negative).  Defaults to 1.
+
+    >>> list(circular_shifts(range(4), 2))
+    [(0, 1, 2, 3), (2, 3, 0, 1)]
+
+    >>> list(circular_shifts(range(4), -1))
+    [(0, 1, 2, 3), (3, 0, 1, 2), (2, 3, 0, 1), (1, 2, 3, 0)]
+
     """
-    lst = list(iterable)
+    buffer = deque(iterable)
-    return take(len(lst), windowed(cycle(lst), len(lst)))
+    if steps == 0:
+        raise ValueError('Steps should be a non-zero integer')
+
+    buffer.rotate(steps)
+    steps = -steps
+    n = len(buffer)
+    n //= math.gcd(n, steps)
+
+    for __ in repeat(None, n):
+        buffer.rotate(steps)
+        yield tuple(buffer)
 
 
 def make_decorator(wrapping_func, result_index=0):
@@ -3191,7 +3279,7 @@ def partitions(iterable):
         yield [sequence[i:j] for i, j in zip((0,) + i, i + (n,))]
 
 
-def set_partitions(iterable, k=None):
+def set_partitions(iterable, k=None, min_size=None, max_size=None):
     """
     Yield the set partitions of *iterable* into *k* parts. Set partitions are
     not order-preserving.
@@ -3215,6 +3303,20 @@ def set_partitions(iterable, k=None):
     ['b', 'ac']
     ['a', 'b', 'c']
 
+    if *min_size* and/or *max_size* are given, the minimum and/or maximum size
+    per block in partition is set.
+
+    >>> iterable = 'abc'
+    >>> for part in set_partitions(iterable, min_size=2):
+    ...     print([''.join(p) for p in part])
+    ['abc']
+    >>> for part in set_partitions(iterable, max_size=2):
+    ...     print([''.join(p) for p in part])
+    ['a', 'bc']
+    ['ab', 'c']
+    ['b', 'ac']
+    ['a', 'b', 'c']
+
     """
     L = list(iterable)
     n = len(L)
@@ -3226,6 +3328,11 @@ def set_partitions(iterable, k=None):
         elif k > n:
             return
 
+    min_size = min_size if min_size is not None else 0
+    max_size = max_size if max_size is not None else n
+    if min_size > max_size:
+        return
+
     def set_partitions_helper(L, k):
         n = len(L)
         if k == 1:
@@ -3242,9 +3349,15 @@ def set_partitions(iterable, k=None):
 
     if k is None:
         for k in range(1, n + 1):
-            yield from set_partitions_helper(L, k)
+            yield from filter(
+                lambda z: all(min_size <= len(bk) <= max_size for bk in z),
+                set_partitions_helper(L, k),
+            )
     else:
-        yield from set_partitions_helper(L, k)
+        yield from filter(
+            lambda z: all(min_size <= len(bk) <= max_size for bk in z),
+            set_partitions_helper(L, k),
+        )
 
 
 class time_limited:
@@ -3535,32 +3648,27 @@ def map_if(iterable, pred, func, func_else=lambda x: x):
         yield func(item) if pred(item) else func_else(item)
 
 
-def _sample_unweighted(iterable, k):
+def _sample_unweighted(iterator, k, strict):
-    # Implementation of "Algorithm L" from the 1994 paper by Kim-Hung Li:
+    # Algorithm L in the 1994 paper by Kim-Hung Li:
     # "Reservoir-Sampling Algorithms of Time Complexity O(n(1+log(N/n)))".
 
-    # Fill up the reservoir (collection of samples) with the first `k` samples
+    reservoir = list(islice(iterator, k))
-    reservoir = take(k, iterable)
+    if strict and len(reservoir) < k:
+        raise ValueError('Sample larger than population')
+    W = 1.0
 
-    # Generate random number that's the largest in a sample of k U(0,1) numbers
+    with suppress(StopIteration):
-    # Largest order statistic: https://en.wikipedia.org/wiki/Order_statistic
+        while True:
-    W = exp(log(random()) / k)
-
-    # The number of elements to skip before changing the reservoir is a random
-    # number with a geometric distribution. Sample it using random() and logs.
-    next_index = k + floor(log(random()) / log(1 - W))
-
-    for index, element in enumerate(iterable, k):
-        if index == next_index:
-            reservoir[randrange(k)] = element
-            # The new W is the largest in a sample of k U(0, `old_W`) numbers
             W *= exp(log(random()) / k)
-            next_index += floor(log(random()) / log(1 - W)) + 1
+            skip = floor(log(random()) / log1p(-W))
+            element = next(islice(iterator, skip, None))
+            reservoir[randrange(k)] = element
 
+    shuffle(reservoir)
     return reservoir
 
 
-def _sample_weighted(iterable, k, weights):
+def _sample_weighted(iterator, k, weights, strict):
     # Implementation of "A-ExpJ" from the 2006 paper by Efraimidis et al. :
     # "Weighted random sampling with a reservoir".
 
@@ -3569,7 +3677,10 @@ def _sample_weighted(iterable, k, weights):
 
     # Fill up the reservoir (collection of samples) with the first `k`
     # weight-keys and elements, then heapify the list.
-    reservoir = take(k, zip(weight_keys, iterable))
+    reservoir = take(k, zip(weight_keys, iterator))
+    if strict and len(reservoir) < k:
+        raise ValueError('Sample larger than population')
+
     heapify(reservoir)
 
     # The number of jumps before changing the reservoir is a random variable
@@ -3577,7 +3688,7 @@ def _sample_weighted(iterable, k, weights):
     smallest_weight_key, _ = reservoir[0]
     weights_to_skip = log(random()) / smallest_weight_key
 
-    for weight, element in zip(weights, iterable):
+    for weight, element in zip(weights, iterator):
         if weight >= weights_to_skip:
             # The notation here is consistent with the paper, but we store
             # the weight-keys in log-space for better numerical stability.
@@ -3591,44 +3702,103 @@ def _sample_weighted(iterable, k, weights):
         else:
             weights_to_skip -= weight
 
-    # Equivalent to [element for weight_key, element in sorted(reservoir)]
+    ret = [element for weight_key, element in reservoir]
-    return [heappop(reservoir)[1] for _ in range(k)]
+    shuffle(ret)
+    return ret
 
 
-def sample(iterable, k, weights=None):
+def _sample_counted(population, k, counts, strict):
+    element = None
+    remaining = 0
+
+    def feed(i):
+        # Advance *i* steps ahead and consume an element
+        nonlocal element, remaining
+
+        while i + 1 > remaining:
+            i = i - remaining
+            element = next(population)
+            remaining = next(counts)
+        remaining -= i + 1
+        return element
+
+    with suppress(StopIteration):
+        reservoir = []
+        for _ in range(k):
+            reservoir.append(feed(0))
+        if strict and len(reservoir) < k:
+            raise ValueError('Sample larger than population')
+
+        W = 1.0
+        while True:
+            W *= exp(log(random()) / k)
+            skip = floor(log(random()) / log1p(-W))
+            element = feed(skip)
+            reservoir[randrange(k)] = element
+
+    shuffle(reservoir)
+    return reservoir
+
+
+def sample(iterable, k, weights=None, *, counts=None, strict=False):
     """Return a *k*-length list of elements chosen (without replacement)
-    from the *iterable*. Like :func:`random.sample`, but works on iterables
+    from the *iterable*. Similar to :func:`random.sample`, but works on
-    of unknown length.
+    iterables of unknown length.
 
     >>> iterable = range(100)
     >>> sample(iterable, 5)  # doctest: +SKIP
     [81, 60, 96, 16, 4]
 
-    An iterable with *weights* may also be given:
+    For iterables with repeated elements, you may supply *counts* to
+    indicate the repeats.
+
+    >>> iterable = ['a', 'b']
+    >>> counts = [3, 4]  # Equivalent to 'a', 'a', 'a', 'b', 'b', 'b', 'b'
+    >>> sample(iterable, k=3, counts=counts)  # doctest: +SKIP
+    ['a', 'a', 'b']
+
+    An iterable with *weights* may be given:
 
     >>> iterable = range(100)
     >>> weights = (i * i + 1 for i in range(100))
     >>> sampled = sample(iterable, 5, weights=weights)  # doctest: +SKIP
     [79, 67, 74, 66, 78]
 
-    The algorithm can also be used to generate weighted random permutations.
+    Weighted selections are made without replacement.
-    The relative weight of each item determines the probability that it
+    After an element is selected, it is removed from the pool and the
-    appears late in the permutation.
+    relative weights of the other elements increase (this
+    does not match the behavior of :func:`random.sample`'s *counts*
+    parameter). Note that *weights* may not be used with *counts*.
 
-    >>> data = "abcdefgh"
+    If the length of *iterable* is less than *k*,
-    >>> weights = range(1, len(data) + 1)
+    ``ValueError`` is raised if *strict* is ``True`` and
-    >>> sample(data, k=len(data), weights=weights)  # doctest: +SKIP
+    all elements are returned (in shuffled order) if *strict* is ``False``.
-    ['c', 'a', 'b', 'e', 'g', 'd', 'h', 'f']
+
+    By default, the `Algorithm L <https://w.wiki/ANrM>`__ reservoir sampling
+    technique is used. When *weights* are provided,
+    `Algorithm A-ExpJ <https://w.wiki/ANrS>`__ is used.
     """
+    iterator = iter(iterable)
+
+    if k < 0:
+        raise ValueError('k must be non-negative')
+
     if k == 0:
         return []
 
-    iterable = iter(iterable)
+    if weights is not None and counts is not None:
-    if weights is None:
+        raise TypeError('weights and counts are mutally exclusive')
-        return _sample_unweighted(iterable, k)
+
-    else:
+    elif weights is not None:
         weights = iter(weights)
-        return _sample_weighted(iterable, k, weights)
+        return _sample_weighted(iterator, k, weights, strict)
+
+    elif counts is not None:
+        counts = iter(counts)
+        return _sample_counted(iterator, k, counts, strict)
+
+    else:
+        return _sample_unweighted(iterator, k, strict)
 
 
 def is_sorted(iterable, key=None, reverse=False, strict=False):
@@ -3650,12 +3820,16 @@ def is_sorted(iterable, key=None, reverse=False, strict=False):
     False
 
     The function returns ``False`` after encountering the first out-of-order
-    item. If there are no out-of-order items, the iterable is exhausted.
+    item, which means it may produce results that differ from the built-in
+    :func:`sorted` function for objects with unusual comparison dynamics.
+    If there are no out-of-order items, the iterable is exhausted.
     """
+    compare = le if strict else lt
+    it = iterable if (key is None) else map(key, iterable)
+    it_1, it_2 = tee(it)
+    next(it_2 if reverse else it_1, None)
 
-    compare = (le if reverse else ge) if strict else (lt if reverse else gt)
+    return not any(map(compare, it_1, it_2))
-    it = iterable if key is None else map(key, iterable)
-    return not any(starmap(compare, pairwise(it)))
 
 
 class AbortThread(BaseException):

lib/more_itertools/more.pyi

@@ -2,6 +2,8 @@
 
 from __future__ import annotations
 
+import sys
+
 from types import TracebackType
 from typing import (
     Any,
@@ -28,6 +30,9 @@ from typing_extensions import Protocol
 _T = TypeVar('_T')
 _T1 = TypeVar('_T1')
 _T2 = TypeVar('_T2')
+_T3 = TypeVar('_T3')
+_T4 = TypeVar('_T4')
+_T5 = TypeVar('_T5')
 _U = TypeVar('_U')
 _V = TypeVar('_V')
 _W = TypeVar('_W')
@@ -35,6 +40,12 @@ _T_co = TypeVar('_T_co', covariant=True)
 _GenFn = TypeVar('_GenFn', bound=Callable[..., Iterator[Any]])
 _Raisable = BaseException | Type[BaseException]
 
+# The type of isinstance's second argument (from typeshed builtins)
+if sys.version_info >= (3, 10):
+    _ClassInfo = type | UnionType | tuple[_ClassInfo, ...]
+else:
+    _ClassInfo = type | tuple[_ClassInfo, ...]
+
 @type_check_only
 class _SizedIterable(Protocol[_T_co], Sized, Iterable[_T_co]): ...
 
@@ -135,7 +146,7 @@ def interleave_evenly(
 ) -> Iterator[_T]: ...
 def collapse(
     iterable: Iterable[Any],
-    base_type: type | None = ...,
+    base_type: _ClassInfo | None = ...,
     levels: int | None = ...,
 ) -> Iterator[Any]: ...
 @overload
@@ -213,6 +224,7 @@ def stagger(
 class UnequalIterablesError(ValueError):
     def __init__(self, details: tuple[int, int, int] | None = ...) -> None: ...
 
+# zip_equal
 @overload
 def zip_equal(__iter1: Iterable[_T1]) -> Iterator[tuple[_T1]]: ...
 @overload
@@ -221,11 +233,35 @@ def zip_equal(
 ) -> Iterator[tuple[_T1, _T2]]: ...
 @overload
 def zip_equal(
-    __iter1: Iterable[_T],
+    __iter1: Iterable[_T1], __iter2: Iterable[_T2], __iter3: Iterable[_T3]
-    __iter2: Iterable[_T],
+) -> Iterator[tuple[_T1, _T2, _T3]]: ...
-    __iter3: Iterable[_T],
+@overload
-    *iterables: Iterable[_T],
+def zip_equal(
-) -> Iterator[tuple[_T, ...]]: ...
+    __iter1: Iterable[_T1],
+    __iter2: Iterable[_T2],
+    __iter3: Iterable[_T3],
+    __iter4: Iterable[_T4],
+) -> Iterator[tuple[_T1, _T2, _T3, _T4]]: ...
+@overload
+def zip_equal(
+    __iter1: Iterable[_T1],
+    __iter2: Iterable[_T2],
+    __iter3: Iterable[_T3],
+    __iter4: Iterable[_T4],
+    __iter5: Iterable[_T5],
+) -> Iterator[tuple[_T1, _T2, _T3, _T4, _T5]]: ...
+@overload
+def zip_equal(
+    __iter1: Iterable[Any],
+    __iter2: Iterable[Any],
+    __iter3: Iterable[Any],
+    __iter4: Iterable[Any],
+    __iter5: Iterable[Any],
+    __iter6: Iterable[Any],
+    *iterables: Iterable[Any],
+) -> Iterator[tuple[Any, ...]]: ...
+
+# zip_offset
 @overload
 def zip_offset(
     __iter1: Iterable[_T1],
@@ -285,12 +321,13 @@ def sort_together(
     key_list: Iterable[int] = ...,
     key: Callable[..., Any] | None = ...,
     reverse: bool = ...,
+    strict: bool = ...,
 ) -> list[tuple[_T, ...]]: ...
 def unzip(iterable: Iterable[Sequence[_T]]) -> tuple[Iterator[_T], ...]: ...
 def divide(n: int, iterable: Iterable[_T]) -> list[Iterator[_T]]: ...
 def always_iterable(
     obj: object,
-    base_type: type | tuple[type | tuple[Any, ...], ...] | None = ...,
+    base_type: _ClassInfo | None = ...,
 ) -> Iterator[Any]: ...
 def adjacent(
     predicate: Callable[[_T], bool],
@@ -454,7 +491,9 @@ class run_length:
 def exactly_n(
     iterable: Iterable[_T], n: int, predicate: Callable[[_T], object] = ...
 ) -> bool: ...
-def circular_shifts(iterable: Iterable[_T]) -> list[tuple[_T, ...]]: ...
+def circular_shifts(
+    iterable: Iterable[_T], steps: int = 1
+) -> list[tuple[_T, ...]]: ...
 def make_decorator(
     wrapping_func: Callable[..., _U], result_index: int = ...
 ) -> Callable[..., Callable[[Callable[..., Any]], Callable[..., _U]]]: ...
@@ -500,7 +539,10 @@ def replace(
 ) -> Iterator[_T | _U]: ...
 def partitions(iterable: Iterable[_T]) -> Iterator[list[list[_T]]]: ...
 def set_partitions(
-    iterable: Iterable[_T], k: int | None = ...
+    iterable: Iterable[_T],
+    k: int | None = ...,
+    min_size: int | None = ...,
+    max_size: int | None = ...,
 ) -> Iterator[list[list[_T]]]: ...
 
 class time_limited(Generic[_T], Iterator[_T]):
@@ -538,10 +580,22 @@ def map_if(
     func: Callable[[Any], Any],
     func_else: Callable[[Any], Any] | None = ...,
 ) -> Iterator[Any]: ...
+def _sample_unweighted(
+    iterator: Iterator[_T], k: int, strict: bool
+) -> list[_T]: ...
+def _sample_counted(
+    population: Iterator[_T], k: int, counts: Iterable[int], strict: bool
+) -> list[_T]: ...
+def _sample_weighted(
+    iterator: Iterator[_T], k: int, weights, strict
+) -> list[_T]: ...
 def sample(
     iterable: Iterable[_T],
     k: int,
     weights: Iterable[float] | None = ...,
+    *,
+    counts: Iterable[int] | None = ...,
+    strict: bool = False,
 ) -> list[_T]: ...
 def is_sorted(
     iterable: Iterable[_T],
@@ -577,7 +631,7 @@ class callback_iter(Generic[_T], Iterator[_T]):
 
 def windowed_complete(
     iterable: Iterable[_T], n: int
-) -> Iterator[tuple[_T, ...]]: ...
+) -> Iterator[tuple[tuple[_T, ...], tuple[_T, ...], tuple[_T, ...]]]: ...
 def all_unique(
     iterable: Iterable[_T], key: Callable[[_T], _U] | None = ...
 ) -> bool: ...
@@ -608,9 +662,61 @@ class countable(Generic[_T], Iterator[_T]):
     items_seen: int
 
 def chunked_even(iterable: Iterable[_T], n: int) -> Iterator[list[_T]]: ...
+@overload
 def zip_broadcast(
+    __obj1: _T | Iterable[_T],
+    *,
+    scalar_types: _ClassInfo | None = ...,
+    strict: bool = ...,
+) -> Iterable[tuple[_T, ...]]: ...
+@overload
+def zip_broadcast(
+    __obj1: _T | Iterable[_T],
+    __obj2: _T | Iterable[_T],
+    *,
+    scalar_types: _ClassInfo | None = ...,
+    strict: bool = ...,
+) -> Iterable[tuple[_T, ...]]: ...
+@overload
+def zip_broadcast(
+    __obj1: _T | Iterable[_T],
+    __obj2: _T | Iterable[_T],
+    __obj3: _T | Iterable[_T],
+    *,
+    scalar_types: _ClassInfo | None = ...,
+    strict: bool = ...,
+) -> Iterable[tuple[_T, ...]]: ...
+@overload
+def zip_broadcast(
+    __obj1: _T | Iterable[_T],
+    __obj2: _T | Iterable[_T],
+    __obj3: _T | Iterable[_T],
+    __obj4: _T | Iterable[_T],
+    *,
+    scalar_types: _ClassInfo | None = ...,
+    strict: bool = ...,
+) -> Iterable[tuple[_T, ...]]: ...
+@overload
+def zip_broadcast(
+    __obj1: _T | Iterable[_T],
+    __obj2: _T | Iterable[_T],
+    __obj3: _T | Iterable[_T],
+    __obj4: _T | Iterable[_T],
+    __obj5: _T | Iterable[_T],
+    *,
+    scalar_types: _ClassInfo | None = ...,
+    strict: bool = ...,
+) -> Iterable[tuple[_T, ...]]: ...
+@overload
+def zip_broadcast(
+    __obj1: _T | Iterable[_T],
+    __obj2: _T | Iterable[_T],
+    __obj3: _T | Iterable[_T],
+    __obj4: _T | Iterable[_T],
+    __obj5: _T | Iterable[_T],
+    __obj6: _T | Iterable[_T],
     *objects: _T | Iterable[_T],
-    scalar_types: type | tuple[type | tuple[Any, ...], ...] | None = ...,
+    scalar_types: _ClassInfo | None = ...,
     strict: bool = ...,
 ) -> Iterable[tuple[_T, ...]]: ...
 def unique_in_window(

lib/more_itertools/recipes.py

@@ -795,8 +795,30 @@ def triplewise(iterable):
     [('A', 'B', 'C'), ('B', 'C', 'D'), ('C', 'D', 'E')]
 
     """
-    for (a, _), (b, c) in pairwise(pairwise(iterable)):
+    # This deviates from the itertools documentation reciple - see
-        yield a, b, c
+    # https://github.com/more-itertools/more-itertools/issues/889
+    t1, t2, t3 = tee(iterable, 3)
+    next(t3, None)
+    next(t3, None)
+    next(t2, None)
+    return zip(t1, t2, t3)
+
+
+def _sliding_window_islice(iterable, n):
+    # Fast path for small, non-zero values of n.
+    iterators = tee(iterable, n)
+    for i, iterator in enumerate(iterators):
+        next(islice(iterator, i, i), None)
+    return zip(*iterators)
+
+
+def _sliding_window_deque(iterable, n):
+    # Normal path for other values of n.
+    it = iter(iterable)
+    window = deque(islice(it, n - 1), maxlen=n)
+    for x in it:
+        window.append(x)
+        yield tuple(window)
 
 
 def sliding_window(iterable, n):
@@ -812,11 +834,16 @@ def sliding_window(iterable, n):
 
     For a variant with more features, see :func:`windowed`.
     """
-    it = iter(iterable)
+    if n > 20:
-    window = deque(islice(it, n - 1), maxlen=n)
+        return _sliding_window_deque(iterable, n)
-    for x in it:
+    elif n > 2:
-        window.append(x)
+        return _sliding_window_islice(iterable, n)
-        yield tuple(window)
+    elif n == 2:
+        return pairwise(iterable)
+    elif n == 1:
+        return zip(iterable)
+    else:
+        raise ValueError(f'n should be at least one, not {n}')
 
 
 def subslices(iterable):
@@ -1038,9 +1065,6 @@ def totient(n):
     >>> totient(12)
     4
     """
-    # The itertools docs use unique_justseen instead of set; see
+    for prime in set(factor(n)):
-    # https://github.com/more-itertools/more-itertools/issues/823
+        n -= n // prime
-    for p in set(factor(n)):
-        n = n // p * (p - 1)
-
     return n

lib/tempora/__init__.py

@@ -10,6 +10,9 @@ from numbers import Number
 from typing import Union, Tuple, Iterable
 from typing import cast
 
+import dateutil.parser
+import dateutil.tz
+
 
 # some useful constants
 osc_per_year = 290_091_329_207_984_000
@@ -611,3 +614,40 @@ def date_range(start=None, stop=None, step=None):
     while start < stop:
         yield start
         start += step
+
+
+tzinfos = dict(
+    AEST=dateutil.tz.gettz("Australia/Sydney"),
+    AEDT=dateutil.tz.gettz("Australia/Sydney"),
+    ACST=dateutil.tz.gettz("Australia/Darwin"),
+    ACDT=dateutil.tz.gettz("Australia/Adelaide"),
+    AWST=dateutil.tz.gettz("Australia/Perth"),
+    EST=dateutil.tz.gettz("America/New_York"),
+    EDT=dateutil.tz.gettz("America/New_York"),
+    CST=dateutil.tz.gettz("America/Chicago"),
+    CDT=dateutil.tz.gettz("America/Chicago"),
+    MST=dateutil.tz.gettz("America/Denver"),
+    MDT=dateutil.tz.gettz("America/Denver"),
+    PST=dateutil.tz.gettz("America/Los_Angeles"),
+    PDT=dateutil.tz.gettz("America/Los_Angeles"),
+    GMT=dateutil.tz.gettz("Etc/GMT"),
+    UTC=dateutil.tz.gettz("UTC"),
+    CET=dateutil.tz.gettz("Europe/Berlin"),
+    CEST=dateutil.tz.gettz("Europe/Berlin"),
+    IST=dateutil.tz.gettz("Asia/Kolkata"),
+    BST=dateutil.tz.gettz("Europe/London"),
+    MSK=dateutil.tz.gettz("Europe/Moscow"),
+    EET=dateutil.tz.gettz("Europe/Helsinki"),
+    EEST=dateutil.tz.gettz("Europe/Helsinki"),
+    # Add more mappings as needed
+)
+
+
+def parse(*args, **kwargs):
+    """
+    Parse the input using dateutil.parser.parse with friendly tz support.
+
+    >>> parse('2024-07-26 12:59:00 EDT')
+    datetime.datetime(...America/New_York...)
+    """
+    return dateutil.parser.parse(*args, tzinfos=tzinfos, **kwargs)

requirements.txt

@@ -39,7 +39,7 @@ requests-oauthlib==2.0.0
 rumps==0.4.0; platform_system == "Darwin"
 simplejson==3.19.2
 six==1.16.0
-tempora==5.6.0
+tempora==5.7.0
 tokenize-rt==5.2.0
 tzdata==2024.1
 tzlocal==5.0.1