add ydb deps

1 files changed, 2333 insertions, 0 deletions

diff --git a/contrib/python/more-itertools/py2/more_itertools/more.py b/contrib/python/more-itertools/py2/more_itertools/more.py
new file mode 100644
index 0000000000..bd32a26130
--- /dev/null
+++ b/contrib/python/more-itertools/py2/more_itertools/more.py
@@ -0,0 +1,2333 @@
+from __future__ import print_function
+
+from collections import Counter, defaultdict, deque
+from functools import partial, wraps
+from heapq import merge
+from itertools import (
+    chain,
+    compress,
+    count,
+    cycle,
+    dropwhile,
+    groupby,
+    islice,
+    repeat,
+    starmap,
+    takewhile,
+    tee
+)
+from operator import itemgetter, lt, gt, sub
+from sys import maxsize, version_info
+try:
+    from collections.abc import Sequence
+except ImportError:
+    from collections import Sequence
+
+from six import binary_type, string_types, text_type
+from six.moves import filter, map, range, zip, zip_longest
+
+from .recipes import consume, flatten, take
+
+__all__ = [
+    'adjacent',
+    'always_iterable',
+    'always_reversible',
+    'bucket',
+    'chunked',
+    'circular_shifts',
+    'collapse',
+    'collate',
+    'consecutive_groups',
+    'consumer',
+    'count_cycle',
+    'difference',
+    'distinct_permutations',
+    'distribute',
+    'divide',
+    'exactly_n',
+    'first',
+    'groupby_transform',
+    'ilen',
+    'interleave_longest',
+    'interleave',
+    'intersperse',
+    'islice_extended',
+    'iterate',
+    'last',
+    'locate',
+    'lstrip',
+    'make_decorator',
+    'map_reduce',
+    'numeric_range',
+    'one',
+    'padded',
+    'peekable',
+    'replace',
+    'rlocate',
+    'rstrip',
+    'run_length',
+    'seekable',
+    'SequenceView',
+    'side_effect',
+    'sliced',
+    'sort_together',
+    'split_at',
+    'split_after',
+    'split_before',
+    'split_into',
+    'spy',
+    'stagger',
+    'strip',
+    'substrings',
+    'unique_to_each',
+    'unzip',
+    'windowed',
+    'with_iter',
+    'zip_offset',
+]
+
+_marker = object()
+
+
+def chunked(iterable, n):
+    """Break *iterable* into lists of length *n*:
+
+        >>> list(chunked([1, 2, 3, 4, 5, 6], 3))
+        [[1, 2, 3], [4, 5, 6]]
+
+    If the length of *iterable* is not evenly divisible by *n*, the last
+    returned list will be shorter:
+
+        >>> list(chunked([1, 2, 3, 4, 5, 6, 7, 8], 3))
+        [[1, 2, 3], [4, 5, 6], [7, 8]]
+
+    To use a fill-in value instead, see the :func:`grouper` recipe.
+
+    :func:`chunked` is useful for splitting up a computation on a large number
+    of keys into batches, to be pickled and sent off to worker processes. One
+    example is operations on rows in MySQL, which does not implement
+    server-side cursors properly and would otherwise load the entire dataset
+    into RAM on the client.
+
+    """
+    return iter(partial(take, n, iter(iterable)), [])
+
+
+def first(iterable, default=_marker):
+    """Return the first item of *iterable*, or *default* if *iterable* is
+    empty.
+
+        >>> first([0, 1, 2, 3])
+        0
+        >>> first([], 'some default')
+        'some default'
+
+    If *default* is not provided and there are no items in the iterable,
+    raise ``ValueError``.
+
+    :func:`first` is useful when you have a generator of expensive-to-retrieve
+    values and want any arbitrary one. It is marginally shorter than
+    ``next(iter(iterable), default)``.
+
+    """
+    try:
+        return next(iter(iterable))
+    except StopIteration:
+        # I'm on the edge about raising ValueError instead of StopIteration. At
+        # the moment, ValueError wins, because the caller could conceivably
+        # want to do something different with flow control when I raise the
+        # exception, and it's weird to explicitly catch StopIteration.
+        if default is _marker:
+            raise ValueError('first() was called on an empty iterable, and no '
+                             'default value was provided.')
+        return default
+
+
+def last(iterable, default=_marker):
+    """Return the last item of *iterable*, or *default* if *iterable* is
+    empty.
+
+        >>> last([0, 1, 2, 3])
+        3
+        >>> last([], 'some default')
+        'some default'
+
+    If *default* is not provided and there are no items in the iterable,
+    raise ``ValueError``.
+    """
+    try:
+        try:
+            # Try to access the last item directly
+            return iterable[-1]
+        except (TypeError, AttributeError, KeyError):
+            # If not slice-able, iterate entirely using length-1 deque
+            return deque(iterable, maxlen=1)[0]
+    except IndexError:  # If the iterable was empty
+        if default is _marker:
+            raise ValueError('last() was called on an empty iterable, and no '
+                             'default value was provided.')
+        return default
+
+
+class peekable(object):
+    """Wrap an iterator to allow lookahead and prepending elements.
+
+    Call :meth:`peek` on the result to get the value that will be returned
+    by :func:`next`. This won't advance the iterator:
+
+        >>> p = peekable(['a', 'b'])
+        >>> p.peek()
+        'a'
+        >>> next(p)
+        'a'
+
+    Pass :meth:`peek` a default value to return that instead of raising
+    ``StopIteration`` when the iterator is exhausted.
+
+        >>> p = peekable([])
+        >>> p.peek('hi')
+        'hi'
+
+    peekables also offer a :meth:`prepend` method, which "inserts" items
+    at the head of the iterable:
+
+        >>> p = peekable([1, 2, 3])
+        >>> p.prepend(10, 11, 12)
+        >>> next(p)
+        10
+        >>> p.peek()
+        11
+        >>> list(p)
+        [11, 12, 1, 2, 3]
+
+    peekables can be indexed. Index 0 is the item that will be returned by
+    :func:`next`, index 1 is the item after that, and so on:
+    The values up to the given index will be cached.
+
+        >>> p = peekable(['a', 'b', 'c', 'd'])
+        >>> p[0]
+        'a'
+        >>> p[1]
+        'b'
+        >>> next(p)
+        'a'
+
+    Negative indexes are supported, but be aware that they will cache the
+    remaining items in the source iterator, which may require significant
+    storage.
+
+    To check whether a peekable is exhausted, check its truth value:
+
+        >>> p = peekable(['a', 'b'])
+        >>> if p:  # peekable has items
+        ...     list(p)
+        ['a', 'b']
+        >>> if not p:  # peekable is exhaused
+        ...     list(p)
+        []
+
+    """
+    def __init__(self, iterable):
+        self._it = iter(iterable)
+        self._cache = deque()
+
+    def __iter__(self):
+        return self
+
+    def __bool__(self):
+        try:
+            self.peek()
+        except StopIteration:
+            return False
+        return True
+
+    def __nonzero__(self):
+        # For Python 2 compatibility
+        return self.__bool__()
+
+    def peek(self, default=_marker):
+        """Return the item that will be next returned from ``next()``.
+
+        Return ``default`` if there are no items left. If ``default`` is not
+        provided, raise ``StopIteration``.
+
+        """
+        if not self._cache:
+            try:
+                self._cache.append(next(self._it))
+            except StopIteration:
+                if default is _marker:
+                    raise
+                return default
+        return self._cache[0]
+
+    def prepend(self, *items):
+        """Stack up items to be the next ones returned from ``next()`` or
+        ``self.peek()``. The items will be returned in
+        first in, first out order::
+
+            >>> p = peekable([1, 2, 3])
+            >>> p.prepend(10, 11, 12)
+            >>> next(p)
+            10
+            >>> list(p)
+            [11, 12, 1, 2, 3]
+
+        It is possible, by prepending items, to "resurrect" a peekable that
+        previously raised ``StopIteration``.
+
+            >>> p = peekable([])
+            >>> next(p)
+            Traceback (most recent call last):
+              ...
+            StopIteration
+            >>> p.prepend(1)
+            >>> next(p)
+            1
+            >>> next(p)
+            Traceback (most recent call last):
+              ...
+            StopIteration
+
+        """
+        self._cache.extendleft(reversed(items))
+
+    def __next__(self):
+        if self._cache:
+            return self._cache.popleft()
+
+        return next(self._it)
+
+    next = __next__  # For Python 2 compatibility
+
+    def _get_slice(self, index):
+        # Normalize the slice's arguments
+        step = 1 if (index.step is None) else index.step
+        if step > 0:
+            start = 0 if (index.start is None) else index.start
+            stop = maxsize if (index.stop is None) else index.stop
+        elif step < 0:
+            start = -1 if (index.start is None) else index.start
+            stop = (-maxsize - 1) if (index.stop is None) else index.stop
+        else:
+            raise ValueError('slice step cannot be zero')
+
+        # If either the start or stop index is negative, we'll need to cache
+        # the rest of the iterable in order to slice from the right side.
+        if (start < 0) or (stop < 0):
+            self._cache.extend(self._it)
+        # Otherwise we'll need to find the rightmost index and cache to that
+        # point.
+        else:
+            n = min(max(start, stop) + 1, maxsize)
+            cache_len = len(self._cache)
+            if n >= cache_len:
+                self._cache.extend(islice(self._it, n - cache_len))
+
+        return list(self._cache)[index]
+
+    def __getitem__(self, index):
+        if isinstance(index, slice):
+            return self._get_slice(index)
+
+        cache_len = len(self._cache)
+        if index < 0:
+            self._cache.extend(self._it)
+        elif index >= cache_len:
+            self._cache.extend(islice(self._it, index + 1 - cache_len))
+
+        return self._cache[index]
+
+
+def _collate(*iterables, **kwargs):
+    """Helper for ``collate()``, called when the user is using the ``reverse``
+    or ``key`` keyword arguments on Python versions below 3.5.
+
+    """
+    key = kwargs.pop('key', lambda a: a)
+    reverse = kwargs.pop('reverse', False)
+
+    min_or_max = partial(max if reverse else min, key=itemgetter(0))
+    peekables = [peekable(it) for it in iterables]
+    peekables = [p for p in peekables if p]  # Kill empties.
+    while peekables:
+        _, p = min_or_max((key(p.peek()), p) for p in peekables)
+        yield next(p)
+        peekables = [x for x in peekables if x]
+
+
+def collate(*iterables, **kwargs):
+    """Return a sorted merge of the items from each of several already-sorted
+    *iterables*.
+
+        >>> list(collate('ACDZ', 'AZ', 'JKL'))
+        ['A', 'A', 'C', 'D', 'J', 'K', 'L', 'Z', 'Z']
+
+    Works lazily, keeping only the next value from each iterable in memory. Use
+    :func:`collate` to, for example, perform a n-way mergesort of items that
+    don't fit in memory.
+
+    If a *key* function is specified, the iterables will be sorted according
+    to its result:
+
+        >>> key = lambda s: int(s)  # Sort by numeric value, not by string
+        >>> list(collate(['1', '10'], ['2', '11'], key=key))
+        ['1', '2', '10', '11']
+
+
+    If the *iterables* are sorted in descending order, set *reverse* to
+    ``True``:
+
+        >>> list(collate([5, 3, 1], [4, 2, 0], reverse=True))
+        [5, 4, 3, 2, 1, 0]
+
+    If the elements of the passed-in iterables are out of order, you might get
+    unexpected results.
+
+    On Python 2.7, this function delegates to :func:`heapq.merge` if neither
+    of the keyword arguments are specified. On Python 3.5+, this function
+    is an alias for :func:`heapq.merge`.
+
+    """
+    if not kwargs:
+        return merge(*iterables)
+
+    return _collate(*iterables, **kwargs)
+
+
+# If using Python version 3.5 or greater, heapq.merge() will be faster than
+# collate - use that instead.
+if version_info >= (3, 5, 0):
+    _collate_docstring = collate.__doc__
+    collate = partial(merge)
+    collate.__doc__ = _collate_docstring
+
+
+def consumer(func):
+    """Decorator that automatically advances a PEP-342-style "reverse iterator"
+    to its first yield point so you don't have to call ``next()`` on it
+    manually.
+
+        >>> @consumer
+        ... def tally():
+        ...     i = 0
+        ...     while True:
+        ...         print('Thing number %s is %s.' % (i, (yield)))
+        ...         i += 1
+        ...
+        >>> t = tally()
+        >>> t.send('red')
+        Thing number 0 is red.
+        >>> t.send('fish')
+        Thing number 1 is fish.
+
+    Without the decorator, you would have to call ``next(t)`` before
+    ``t.send()`` could be used.
+
+    """
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        gen = func(*args, **kwargs)
+        next(gen)
+        return gen
+    return wrapper
+
+
+def ilen(iterable):
+    """Return the number of items in *iterable*.
+
+        >>> ilen(x for x in range(1000000) if x % 3 == 0)
+        333334
+
+    This consumes the iterable, so handle with care.
+
+    """
+    # This approach was selected because benchmarks showed it's likely the
+    # fastest of the known implementations at the time of writing.
+    # See GitHub tracker: #236, #230.
+    counter = count()
+    deque(zip(iterable, counter), maxlen=0)
+    return next(counter)
+
+
+def iterate(func, start):
+    """Return ``start``, ``func(start)``, ``func(func(start))``, ...
+
+        >>> from itertools import islice
+        >>> list(islice(iterate(lambda x: 2*x, 1), 10))
+        [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]
+
+    """
+    while True:
+        yield start
+        start = func(start)
+
+
+def with_iter(context_manager):
+    """Wrap an iterable in a ``with`` statement, so it closes once exhausted.
+
+    For example, this will close the file when the iterator is exhausted::
+
+        upper_lines = (line.upper() for line in with_iter(open('foo')))
+
+    Any context manager which returns an iterable is a candidate for
+    ``with_iter``.
+
+    """
+    with context_manager as iterable:
+        for item in iterable:
+            yield item
+
+
+def one(iterable, too_short=None, too_long=None):
+    """Return the first item from *iterable*, which is expected to contain only
+    that item. Raise an exception if *iterable* is empty or has more than one
+    item.
+
+    :func:`one` is useful for ensuring that an iterable contains only one item.
+    For example, it can be used to retrieve the result of a database query
+    that is expected to return a single row.
+
+    If *iterable* is empty, ``ValueError`` will be raised. You may specify a
+    different exception with the *too_short* keyword:
+
+        >>> it = []
+        >>> one(it)  # doctest: +IGNORE_EXCEPTION_DETAIL
+        Traceback (most recent call last):
+        ...
+        ValueError: too many items in iterable (expected 1)'
+        >>> too_short = IndexError('too few items')
+        >>> one(it, too_short=too_short)  # doctest: +IGNORE_EXCEPTION_DETAIL
+        Traceback (most recent call last):
+        ...
+        IndexError: too few items
+
+    Similarly, if *iterable* contains more than one item, ``ValueError`` will
+    be raised. You may specify a different exception with the *too_long*
+    keyword:
+
+        >>> it = ['too', 'many']
+        >>> one(it)  # doctest: +IGNORE_EXCEPTION_DETAIL
+        Traceback (most recent call last):
+        ...
+        ValueError: too many items in iterable (expected 1)'
+        >>> too_long = RuntimeError
+        >>> one(it, too_long=too_long)  # doctest: +IGNORE_EXCEPTION_DETAIL
+        Traceback (most recent call last):
+        ...
+        RuntimeError
+
+    Note that :func:`one` attempts to advance *iterable* twice to ensure there
+    is only one item. If there is more than one, both items will be discarded.
+    See :func:`spy` or :func:`peekable` to check iterable contents less
+    destructively.
+
+    """
+    it = iter(iterable)
+
+    try:
+        value = next(it)
+    except StopIteration:
+        raise too_short or ValueError('too few items in iterable (expected 1)')
+
+    try:
+        next(it)
+    except StopIteration:
+        pass
+    else:
+        raise too_long or ValueError('too many items in iterable (expected 1)')
+
+    return value
+
+
+def distinct_permutations(iterable):
+    """Yield successive distinct permutations of the elements in *iterable*.
+
+        >>> sorted(distinct_permutations([1, 0, 1]))
+        [(0, 1, 1), (1, 0, 1), (1, 1, 0)]
+
+    Equivalent to ``set(permutations(iterable))``, except duplicates are not
+    generated and thrown away. For larger input sequences this is much more
+    efficient.
+
+    Duplicate permutations arise when there are duplicated elements in the
+    input iterable. The number of items returned is
+    `n! / (x_1! * x_2! * ... * x_n!)`, where `n` is the total number of
+    items input, and each `x_i` is the count of a distinct item in the input
+    sequence.
+
+    """
+    def perm_unique_helper(item_counts, perm, i):
+        """Internal helper function
+
+        :arg item_counts: Stores the unique items in ``iterable`` and how many
+            times they are repeated
+        :arg perm: The permutation that is being built for output
+        :arg i: The index of the permutation being modified
+
+        The output permutations are built up recursively; the distinct items
+        are placed until their repetitions are exhausted.
+        """
+        if i < 0:
+            yield tuple(perm)
+        else:
+            for item in item_counts:
+                if item_counts[item] <= 0:
+                    continue
+                perm[i] = item
+                item_counts[item] -= 1
+                for x in perm_unique_helper(item_counts, perm, i - 1):
+                    yield x
+                item_counts[item] += 1
+
+    item_counts = Counter(iterable)
+    length = sum(item_counts.values())
+
+    return perm_unique_helper(item_counts, [None] * length, length - 1)
+
+
+def intersperse(e, iterable, n=1):
+    """Intersperse filler element *e* among the items in *iterable*, leaving
+    *n* items between each filler element.
+
+        >>> list(intersperse('!', [1, 2, 3, 4, 5]))
+        [1, '!', 2, '!', 3, '!', 4, '!', 5]
+
+        >>> list(intersperse(None, [1, 2, 3, 4, 5], n=2))
+        [1, 2, None, 3, 4, None, 5]
+
+    """
+    if n == 0:
+        raise ValueError('n must be > 0')
+    elif n == 1:
+        # interleave(repeat(e), iterable) -> e, x_0, e, e, x_1, e, x_2...
+        # islice(..., 1, None) -> x_0, e, e, x_1, e, x_2...
+        return islice(interleave(repeat(e), iterable), 1, None)
+    else:
+        # interleave(filler, chunks) -> [e], [x_0, x_1], [e], [x_2, x_3]...
+        # islice(..., 1, None) -> [x_0, x_1], [e], [x_2, x_3]...
+        # flatten(...) -> x_0, x_1, e, x_2, x_3...
+        filler = repeat([e])
+        chunks = chunked(iterable, n)
+        return flatten(islice(interleave(filler, chunks), 1, None))
+
+
+def unique_to_each(*iterables):
+    """Return the elements from each of the input iterables that aren't in the
+    other input iterables.
+
+    For example, suppose you have a set of packages, each with a set of
+    dependencies::
+
+        {'pkg_1': {'A', 'B'}, 'pkg_2': {'B', 'C'}, 'pkg_3': {'B', 'D'}}
+
+    If you remove one package, which dependencies can also be removed?
+
+    If ``pkg_1`` is removed, then ``A`` is no longer necessary - it is not
+    associated with ``pkg_2`` or ``pkg_3``. Similarly, ``C`` is only needed for
+    ``pkg_2``, and ``D`` is only needed for ``pkg_3``::
+
+        >>> unique_to_each({'A', 'B'}, {'B', 'C'}, {'B', 'D'})
+        [['A'], ['C'], ['D']]
+
+    If there are duplicates in one input iterable that aren't in the others
+    they will be duplicated in the output. Input order is preserved::
+
+        >>> unique_to_each("mississippi", "missouri")
+        [['p', 'p'], ['o', 'u', 'r']]
+
+    It is assumed that the elements of each iterable are hashable.
+
+    """
+    pool = [list(it) for it in iterables]
+    counts = Counter(chain.from_iterable(map(set, pool)))
+    uniques = {element for element in counts if counts[element] == 1}
+    return [list(filter(uniques.__contains__, it)) for it in pool]
+
+
+def windowed(seq, n, fillvalue=None, step=1):
+    """Return a sliding window of width *n* over the given iterable.
+
+        >>> all_windows = windowed([1, 2, 3, 4, 5], 3)
+        >>> list(all_windows)
+        [(1, 2, 3), (2, 3, 4), (3, 4, 5)]
+
+    When the window is larger than the iterable, *fillvalue* is used in place
+    of missing values::
+
+        >>> list(windowed([1, 2, 3], 4))
+        [(1, 2, 3, None)]
+
+    Each window will advance in increments of *step*:
+
+        >>> list(windowed([1, 2, 3, 4, 5, 6], 3, fillvalue='!', step=2))
+        [(1, 2, 3), (3, 4, 5), (5, 6, '!')]
+
+    """
+    if n < 0:
+        raise ValueError('n must be >= 0')
+    if n == 0:
+        yield tuple()
+        return
+    if step < 1:
+        raise ValueError('step must be >= 1')
+
+    it = iter(seq)
+    window = deque([], n)
+    append = window.append
+
+    # Initial deque fill
+    for _ in range(n):
+        append(next(it, fillvalue))
+    yield tuple(window)
+
+    # Appending new items to the right causes old items to fall off the left
+    i = 0
+    for item in it:
+        append(item)
+        i = (i + 1) % step
+        if i % step == 0:
+            yield tuple(window)
+
+    # If there are items from the iterable in the window, pad with the given
+    # value and emit them.
+    if (i % step) and (step - i < n):
+        for _ in range(step - i):
+            append(fillvalue)
+        yield tuple(window)
+
+
+def substrings(iterable, join_func=None):
+    """Yield all of the substrings of *iterable*.
+
+        >>> [''.join(s) for s in substrings('more')]
+        ['m', 'o', 'r', 'e', 'mo', 'or', 're', 'mor', 'ore', 'more']
+
+    Note that non-string iterables can also be subdivided.
+
+        >>> list(substrings([0, 1, 2]))
+        [(0,), (1,), (2,), (0, 1), (1, 2), (0, 1, 2)]
+
+    """
+    # The length-1 substrings
+    seq = []
+    for item in iter(iterable):
+        seq.append(item)
+        yield (item,)
+    seq = tuple(seq)
+    item_count = len(seq)
+
+    # And the rest
+    for n in range(2, item_count + 1):
+        for i in range(item_count - n + 1):
+            yield seq[i:i + n]
+
+
+class bucket(object):
+    """Wrap *iterable* and return an object that buckets it iterable into
+    child iterables based on a *key* function.
+
+        >>> iterable = ['a1', 'b1', 'c1', 'a2', 'b2', 'c2', 'b3']
+        >>> s = bucket(iterable, key=lambda x: x[0])
+        >>> a_iterable = s['a']
+        >>> next(a_iterable)
+        'a1'
+        >>> next(a_iterable)
+        'a2'
+        >>> list(s['b'])
+        ['b1', 'b2', 'b3']
+
+    The original iterable will be advanced and its items will be cached until
+    they are used by the child iterables. This may require significant storage.
+
+    By default, attempting to select a bucket to which no items belong  will
+    exhaust the iterable and cache all values.
+    If you specify a *validator* function, selected buckets will instead be
+    checked against it.
+
+        >>> from itertools import count
+        >>> it = count(1, 2)  # Infinite sequence of odd numbers
+        >>> key = lambda x: x % 10  # Bucket by last digit
+        >>> validator = lambda x: x in {1, 3, 5, 7, 9}  # Odd digits only
+        >>> s = bucket(it, key=key, validator=validator)
+        >>> 2 in s
+        False
+        >>> list(s[2])
+        []
+
+    """
+    def __init__(self, iterable, key, validator=None):
+        self._it = iter(iterable)
+        self._key = key
+        self._cache = defaultdict(deque)
+        self._validator = validator or (lambda x: True)
+
+    def __contains__(self, value):
+        if not self._validator(value):
+            return False
+
+        try:
+            item = next(self[value])
+        except StopIteration:
+            return False
+        else:
+            self._cache[value].appendleft(item)
+
+        return True
+
+    def _get_values(self, value):
+        """
+        Helper to yield items from the parent iterator that match *value*.
+        Items that don't match are stored in the local cache as they
+        are encountered.
+        """
+        while True:
+            # If we've cached some items that match the target value, emit
+            # the first one and evict it from the cache.
+            if self._cache[value]:
+                yield self._cache[value].popleft()
+            # Otherwise we need to advance the parent iterator to search for
+            # a matching item, caching the rest.
+            else:
+                while True:
+                    try:
+                        item = next(self._it)
+                    except StopIteration:
+                        return
+                    item_value = self._key(item)
+                    if item_value == value:
+                        yield item
+                        break
+                    elif self._validator(item_value):
+                        self._cache[item_value].append(item)
+
+    def __getitem__(self, value):
+        if not self._validator(value):
+            return iter(())
+
+        return self._get_values(value)
+
+
+def spy(iterable, n=1):
+    """Return a 2-tuple with a list containing the first *n* elements of
+    *iterable*, and an iterator with the same items as *iterable*.
+    This allows you to "look ahead" at the items in the iterable without
+    advancing it.
+
+    There is one item in the list by default:
+
+        >>> iterable = 'abcdefg'
+        >>> head, iterable = spy(iterable)
+        >>> head
+        ['a']
+        >>> list(iterable)
+        ['a', 'b', 'c', 'd', 'e', 'f', 'g']
+
+    You may use unpacking to retrieve items instead of lists:
+
+        >>> (head,), iterable = spy('abcdefg')
+        >>> head
+        'a'
+        >>> (first, second), iterable = spy('abcdefg', 2)
+        >>> first
+        'a'
+        >>> second
+        'b'
+
+    The number of items requested can be larger than the number of items in
+    the iterable:
+
+        >>> iterable = [1, 2, 3, 4, 5]
+        >>> head, iterable = spy(iterable, 10)
+        >>> head
+        [1, 2, 3, 4, 5]
+        >>> list(iterable)
+        [1, 2, 3, 4, 5]
+
+    """
+    it = iter(iterable)
+    head = take(n, it)
+
+    return head, chain(head, it)
+
+
+def interleave(*iterables):
+    """Return a new iterable yielding from each iterable in turn,
+    until the shortest is exhausted.
+
+        >>> list(interleave([1, 2, 3], [4, 5], [6, 7, 8]))
+        [1, 4, 6, 2, 5, 7]
+
+    For a version that doesn't terminate after the shortest iterable is
+    exhausted, see :func:`interleave_longest`.
+
+    """
+    return chain.from_iterable(zip(*iterables))
+
+
+def interleave_longest(*iterables):
+    """Return a new iterable yielding from each iterable in turn,
+    skipping any that are exhausted.
+
+        >>> list(interleave_longest([1, 2, 3], [4, 5], [6, 7, 8]))
+        [1, 4, 6, 2, 5, 7, 3, 8]
+
+    This function produces the same output as :func:`roundrobin`, but may
+    perform better for some inputs (in particular when the number of iterables
+    is large).
+
+    """
+    i = chain.from_iterable(zip_longest(*iterables, fillvalue=_marker))
+    return (x for x in i if x is not _marker)
+
+
+def collapse(iterable, base_type=None, levels=None):
+    """Flatten an iterable with multiple levels of nesting (e.g., a list of
+    lists of tuples) into non-iterable types.
+
+        >>> iterable = [(1, 2), ([3, 4], [[5], [6]])]
+        >>> list(collapse(iterable))
+        [1, 2, 3, 4, 5, 6]
+
+    String types are not considered iterable and will not be collapsed.
+    To avoid collapsing other types, specify *base_type*:
+
+        >>> iterable = ['ab', ('cd', 'ef'), ['gh', 'ij']]
+        >>> list(collapse(iterable, base_type=tuple))
+        ['ab', ('cd', 'ef'), 'gh', 'ij']
+
+    Specify *levels* to stop flattening after a certain level:
+
+    >>> iterable = [('a', ['b']), ('c', ['d'])]
+    >>> list(collapse(iterable))  # Fully flattened
+    ['a', 'b', 'c', 'd']
+    >>> list(collapse(iterable, levels=1))  # Only one level flattened
+    ['a', ['b'], 'c', ['d']]
+
+    """
+    def walk(node, level):
+        if (
+            ((levels is not None) and (level > levels)) or
+            isinstance(node, string_types) or
+            ((base_type is not None) and isinstance(node, base_type))
+        ):
+            yield node
+            return
+
+        try:
+            tree = iter(node)
+        except TypeError:
+            yield node
+            return
+        else:
+            for child in tree:
+                for x in walk(child, level + 1):
+                    yield x
+
+    for x in walk(iterable, 0):
+        yield x
+
+
+def side_effect(func, iterable, chunk_size=None, before=None, after=None):
+    """Invoke *func* on each item in *iterable* (or on each *chunk_size* group
+    of items) before yielding the item.
+
+    `func` must be a function that takes a single argument. Its return value
+    will be discarded.
+
+    *before* and *after* are optional functions that take no arguments. They
+    will be executed before iteration starts and after it ends, respectively.
+
+    `side_effect` can be used for logging, updating progress bars, or anything
+    that is not functionally "pure."
+
+    Emitting a status message:
+
+        >>> from more_itertools import consume
+        >>> func = lambda item: print('Received {}'.format(item))
+        >>> consume(side_effect(func, range(2)))
+        Received 0
+        Received 1
+
+    Operating on chunks of items:
+
+        >>> pair_sums = []
+        >>> func = lambda chunk: pair_sums.append(sum(chunk))
+        >>> list(side_effect(func, [0, 1, 2, 3, 4, 5], 2))
+        [0, 1, 2, 3, 4, 5]
+        >>> list(pair_sums)
+        [1, 5, 9]
+
+    Writing to a file-like object:
+
+        >>> from io import StringIO
+        >>> from more_itertools import consume
+        >>> f = StringIO()
+        >>> func = lambda x: print(x, file=f)
+        >>> before = lambda: print(u'HEADER', file=f)
+        >>> after = f.close
+        >>> it = [u'a', u'b', u'c']
+        >>> consume(side_effect(func, it, before=before, after=after))
+        >>> f.closed
+        True
+
+    """
+    try:
+        if before is not None:
+            before()
+
+        if chunk_size is None:
+            for item in iterable:
+                func(item)
+                yield item
+        else:
+            for chunk in chunked(iterable, chunk_size):
+                func(chunk)
+                for item in chunk:
+                    yield item
+    finally:
+        if after is not None:
+            after()
+
+
+def sliced(seq, n):
+    """Yield slices of length *n* from the sequence *seq*.
+
+        >>> list(sliced((1, 2, 3, 4, 5, 6), 3))
+        [(1, 2, 3), (4, 5, 6)]
+
+    If the length of the sequence is not divisible by the requested slice
+    length, the last slice will be shorter.
+
+        >>> list(sliced((1, 2, 3, 4, 5, 6, 7, 8), 3))
+        [(1, 2, 3), (4, 5, 6), (7, 8)]
+
+    This function will only work for iterables that support slicing.
+    For non-sliceable iterables, see :func:`chunked`.
+
+    """
+    return takewhile(bool, (seq[i: i + n] for i in count(0, n)))
+
+
+def split_at(iterable, pred):
+    """Yield lists of items from *iterable*, where each list is delimited by
+    an item where callable *pred* returns ``True``. The lists do not include
+    the delimiting items.
+
+        >>> list(split_at('abcdcba', lambda x: x == 'b'))
+        [['a'], ['c', 'd', 'c'], ['a']]
+
+        >>> list(split_at(range(10), lambda n: n % 2 == 1))
+        [[0], [2], [4], [6], [8], []]
+    """
+    buf = []
+    for item in iterable:
+        if pred(item):
+            yield buf
+            buf = []
+        else:
+            buf.append(item)
+    yield buf
+
+
+def split_before(iterable, pred):
+    """Yield lists of items from *iterable*, where each list starts with an
+    item where callable *pred* returns ``True``:
+
+        >>> list(split_before('OneTwo', lambda s: s.isupper()))
+        [['O', 'n', 'e'], ['T', 'w', 'o']]
+
+        >>> list(split_before(range(10), lambda n: n % 3 == 0))
+        [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
+
+    """
+    buf = []
+    for item in iterable:
+        if pred(item) and buf:
+            yield buf
+            buf = []
+        buf.append(item)
+    yield buf
+
+
+def split_after(iterable, pred):
+    """Yield lists of items from *iterable*, where each list ends with an
+    item where callable *pred* returns ``True``:
+
+        >>> list(split_after('one1two2', lambda s: s.isdigit()))
+        [['o', 'n', 'e', '1'], ['t', 'w', 'o', '2']]
+
+        >>> list(split_after(range(10), lambda n: n % 3 == 0))
+        [[0], [1, 2, 3], [4, 5, 6], [7, 8, 9]]
+
+    """
+    buf = []
+    for item in iterable:
+        buf.append(item)
+        if pred(item) and buf:
+            yield buf
+            buf = []
+    if buf:
+        yield buf
+
+
+def split_into(iterable, sizes):
+    """Yield a list of sequential items from *iterable* of length 'n' for each
+    integer 'n' in *sizes*.
+
+        >>> list(split_into([1,2,3,4,5,6], [1,2,3]))
+        [[1], [2, 3], [4, 5, 6]]
+
+    If the sum of *sizes* is smaller than the length of *iterable*, then the
+    remaining items of *iterable* will not be returned.
+
+        >>> list(split_into([1,2,3,4,5,6], [2,3]))
+        [[1, 2], [3, 4, 5]]
+
+    If the sum of *sizes* is larger than the length of *iterable*, fewer items
+    will be returned in the iteration that overruns *iterable* and further
+    lists will be empty:
+
+        >>> list(split_into([1,2,3,4], [1,2,3,4]))
+        [[1], [2, 3], [4], []]
+
+    When a ``None`` object is encountered in *sizes*, the returned list will
+    contain items up to the end of *iterable* the same way that itertools.slice
+    does:
+
+        >>> list(split_into([1,2,3,4,5,6,7,8,9,0], [2,3,None]))
+        [[1, 2], [3, 4, 5], [6, 7, 8, 9, 0]]
+
+    :func:`split_into` can be useful for grouping a series of items where the
+    sizes of the groups are not uniform. An example would be where in a row
+    from a table, multiple columns represent elements of the same feature
+    (e.g. a point represented by x,y,z) but, the format is not the same for
+    all columns.
+    """
+    # convert the iterable argument into an iterator so its contents can
+    # be consumed by islice in case it is a generator
+    it = iter(iterable)
+
+    for size in sizes:
+        if size is None:
+            yield list(it)
+            return
+        else:
+            yield list(islice(it, size))
+
+
+def padded(iterable, fillvalue=None, n=None, next_multiple=False):
+    """Yield the elements from *iterable*, followed by *fillvalue*, such that
+    at least *n* items are emitted.
+
+        >>> list(padded([1, 2, 3], '?', 5))
+        [1, 2, 3, '?', '?']
+
+    If *next_multiple* is ``True``, *fillvalue* will be emitted until the
+    number of items emitted is a multiple of *n*::
+
+        >>> list(padded([1, 2, 3, 4], n=3, next_multiple=True))
+        [1, 2, 3, 4, None, None]
+
+    If *n* is ``None``, *fillvalue* will be emitted indefinitely.
+
+    """
+    it = iter(iterable)
+    if n is None:
+        for item in chain(it, repeat(fillvalue)):
+            yield item
+    elif n < 1:
+        raise ValueError('n must be at least 1')
+    else:
+        item_count = 0
+        for item in it:
+            yield item
+            item_count += 1
+
+        remaining = (n - item_count) % n if next_multiple else n - item_count
+        for _ in range(remaining):
+            yield fillvalue
+
+
+def distribute(n, iterable):
+    """Distribute the items from *iterable* among *n* smaller iterables.
+
+        >>> group_1, group_2 = distribute(2, [1, 2, 3, 4, 5, 6])
+        >>> list(group_1)
+        [1, 3, 5]
+        >>> list(group_2)
+        [2, 4, 6]
+
+    If the length of *iterable* is not evenly divisible by *n*, then the
+    length of the returned iterables will not be identical:
+
+        >>> children = distribute(3, [1, 2, 3, 4, 5, 6, 7])
+        >>> [list(c) for c in children]
+        [[1, 4, 7], [2, 5], [3, 6]]
+
+    If the length of *iterable* is smaller than *n*, then the last returned
+    iterables will be empty:
+
+        >>> children = distribute(5, [1, 2, 3])
+        >>> [list(c) for c in children]
+        [[1], [2], [3], [], []]
+
+    This function uses :func:`itertools.tee` and may require significant
+    storage. If you need the order items in the smaller iterables to match the
+    original iterable, see :func:`divide`.
+
+    """
+    if n < 1:
+        raise ValueError('n must be at least 1')
+
+    children = tee(iterable, n)
+    return [islice(it, index, None, n) for index, it in enumerate(children)]
+
+
+def stagger(iterable, offsets=(-1, 0, 1), longest=False, fillvalue=None):
+    """Yield tuples whose elements are offset from *iterable*.
+    The amount by which the `i`-th item in each tuple is offset is given by
+    the `i`-th item in *offsets*.
+
+        >>> list(stagger([0, 1, 2, 3]))
+        [(None, 0, 1), (0, 1, 2), (1, 2, 3)]
+        >>> list(stagger(range(8), offsets=(0, 2, 4)))
+        [(0, 2, 4), (1, 3, 5), (2, 4, 6), (3, 5, 7)]
+
+    By default, the sequence will end when the final element of a tuple is the
+    last item in the iterable. To continue until the first element of a tuple
+    is the last item in the iterable, set *longest* to ``True``::
+
+        >>> list(stagger([0, 1, 2, 3], longest=True))
+        [(None, 0, 1), (0, 1, 2), (1, 2, 3), (2, 3, None), (3, None, None)]
+
+    By default, ``None`` will be used to replace offsets beyond the end of the
+    sequence. Specify *fillvalue* to use some other value.
+
+    """
+    children = tee(iterable, len(offsets))
+
+    return zip_offset(
+        *children, offsets=offsets, longest=longest, fillvalue=fillvalue
+    )
+
+
+def zip_offset(*iterables, **kwargs):
+    """``zip`` the input *iterables* together, but offset the `i`-th iterable
+    by the `i`-th item in *offsets*.
+
+        >>> list(zip_offset('0123', 'abcdef', offsets=(0, 1)))
+        [('0', 'b'), ('1', 'c'), ('2', 'd'), ('3', 'e')]
+
+    This can be used as a lightweight alternative to SciPy or pandas to analyze
+    data sets in which some series have a lead or lag relationship.
+
+    By default, the sequence will end when the shortest iterable is exhausted.
+    To continue until the longest iterable is exhausted, set *longest* to
+    ``True``.
+
+        >>> list(zip_offset('0123', 'abcdef', offsets=(0, 1), longest=True))
+        [('0', 'b'), ('1', 'c'), ('2', 'd'), ('3', 'e'), (None, 'f')]
+
+    By default, ``None`` will be used to replace offsets beyond the end of the
+    sequence. Specify *fillvalue* to use some other value.
+
+    """
+    offsets = kwargs['offsets']
+    longest = kwargs.get('longest', False)
+    fillvalue = kwargs.get('fillvalue', None)
+
+    if len(iterables) != len(offsets):
+        raise ValueError("Number of iterables and offsets didn't match")
+
+    staggered = []
+    for it, n in zip(iterables, offsets):
+        if n < 0:
+            staggered.append(chain(repeat(fillvalue, -n), it))
+        elif n > 0:
+            staggered.append(islice(it, n, None))
+        else:
+            staggered.append(it)
+
+    if longest:
+        return zip_longest(*staggered, fillvalue=fillvalue)
+
+    return zip(*staggered)
+
+
+def sort_together(iterables, key_list=(0,), reverse=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.
+
+    This can be used like the sorting function in a spreadsheet. If each
+    iterable represents a column of data, the key list determines which
+    columns are used for sorting.
+
+    By default, all iterables are sorted using the ``0``-th iterable::
+
+        >>> iterables = [(4, 3, 2, 1), ('a', 'b', 'c', 'd')]
+        >>> sort_together(iterables)
+        [(1, 2, 3, 4), ('d', 'c', 'b', 'a')]
+
+    Set a different key list to sort according to another iterable.
+    Specifying multiple keys dictates how ties are broken::
+
+        >>> iterables = [(3, 1, 2), (0, 1, 0), ('c', 'b', 'a')]
+        >>> sort_together(iterables, key_list=(1, 2))
+        [(2, 3, 1), (0, 0, 1), ('a', 'c', 'b')]
+
+    Set *reverse* to ``True`` to sort in descending order.
+
+        >>> sort_together([(1, 2, 3), ('c', 'b', 'a')], reverse=True)
+        [(3, 2, 1), ('a', 'b', 'c')]
+
+    """
+    return list(zip(*sorted(zip(*iterables),
+                            key=itemgetter(*key_list),
+                            reverse=reverse)))
+
+
+def unzip(iterable):
+    """The inverse of :func:`zip`, this function disaggregates the elements
+    of the zipped *iterable*.
+
+    The ``i``-th iterable contains the ``i``-th element from each element
+    of the zipped iterable. The first element is used to to determine the
+    length of the remaining elements.
+
+        >>> iterable = [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
+        >>> letters, numbers = unzip(iterable)
+        >>> list(letters)
+        ['a', 'b', 'c', 'd']
+        >>> list(numbers)
+        [1, 2, 3, 4]
+
+    This is similar to using ``zip(*iterable)``, but it avoids reading
+    *iterable* into memory. Note, however, that this function uses
+    :func:`itertools.tee` and thus may require significant storage.
+
+    """
+    head, iterable = spy(iter(iterable))
+    if not head:
+        # empty iterable, e.g. zip([], [], [])
+        return ()
+    # spy returns a one-length iterable as head
+    head = head[0]
+    iterables = tee(iterable, len(head))
+
+    def itemgetter(i):
+        def getter(obj):
+            try:
+                return obj[i]
+            except IndexError:
+                # basically if we have an iterable like
+                # iter([(1, 2, 3), (4, 5), (6,)])
+                # the second unzipped iterable would fail at the third tuple
+                # since it would try to access tup[1]
+                # same with the third unzipped iterable and the second tuple
+                # to support these "improperly zipped" iterables,
+                # we create a custom itemgetter
+                # which just stops the unzipped iterables
+                # at first length mismatch
+                raise StopIteration
+        return getter
+
+    return tuple(map(itemgetter(i), it) for i, it in enumerate(iterables))
+
+
+def divide(n, iterable):
+    """Divide the elements from *iterable* into *n* parts, maintaining
+    order.
+
+        >>> group_1, group_2 = divide(2, [1, 2, 3, 4, 5, 6])
+        >>> list(group_1)
+        [1, 2, 3]
+        >>> list(group_2)
+        [4, 5, 6]
+
+    If the length of *iterable* is not evenly divisible by *n*, then the
+    length of the returned iterables will not be identical:
+
+        >>> children = divide(3, [1, 2, 3, 4, 5, 6, 7])
+        >>> [list(c) for c in children]
+        [[1, 2, 3], [4, 5], [6, 7]]
+
+    If the length of the iterable is smaller than n, then the last returned
+    iterables will be empty:
+
+        >>> children = divide(5, [1, 2, 3])
+        >>> [list(c) for c in children]
+        [[1], [2], [3], [], []]
+
+    This function will exhaust the iterable before returning and may require
+    significant storage. If order is not important, see :func:`distribute`,
+    which does not first pull the iterable into memory.
+
+    """
+    if n < 1:
+        raise ValueError('n must be at least 1')
+
+    seq = tuple(iterable)
+    q, r = divmod(len(seq), n)
+
+    ret = []
+    for i in range(n):
+        start = (i * q) + (i if i < r else r)
+        stop = ((i + 1) * q) + (i + 1 if i + 1 < r else r)
+        ret.append(iter(seq[start:stop]))
+
+    return ret
+
+
+def always_iterable(obj, base_type=(text_type, binary_type)):
+    """If *obj* is iterable, return an iterator over its items::
+
+        >>> obj = (1, 2, 3)
+        >>> list(always_iterable(obj))
+        [1, 2, 3]
+
+    If *obj* is not iterable, return a one-item iterable containing *obj*::
+
+        >>> obj = 1
+        >>> list(always_iterable(obj))
+        [1]
+
+    If *obj* is ``None``, return an empty iterable:
+
+        >>> obj = None
+        >>> list(always_iterable(None))
+        []
+
+    By default, binary and text strings are not considered iterable::
+
+        >>> obj = 'foo'
+        >>> list(always_iterable(obj))
+        ['foo']
+
+    If *base_type* is set, objects for which ``isinstance(obj, base_type)``
+    returns ``True`` won't be considered iterable.
+
+        >>> obj = {'a': 1}
+        >>> list(always_iterable(obj))  # Iterate over the dict's keys
+        ['a']
+        >>> list(always_iterable(obj, base_type=dict))  # Treat dicts as a unit
+        [{'a': 1}]
+
+    Set *base_type* to ``None`` to avoid any special handling and treat objects
+    Python considers iterable as iterable:
+
+        >>> obj = 'foo'
+        >>> list(always_iterable(obj, base_type=None))
+        ['f', 'o', 'o']
+    """
+    if obj is None:
+        return iter(())
+
+    if (base_type is not None) and isinstance(obj, base_type):
+        return iter((obj,))
+
+    try:
+        return iter(obj)
+    except TypeError:
+        return iter((obj,))
+
+
+def adjacent(predicate, iterable, distance=1):
+    """Return an iterable over `(bool, item)` tuples where the `item` is
+    drawn from *iterable* and the `bool` indicates whether
+    that item satisfies the *predicate* or is adjacent to an item that does.
+
+    For example, to find whether items are adjacent to a ``3``::
+
+        >>> list(adjacent(lambda x: x == 3, range(6)))
+        [(False, 0), (False, 1), (True, 2), (True, 3), (True, 4), (False, 5)]
+
+    Set *distance* to change what counts as adjacent. For example, to find
+    whether items are two places away from a ``3``:
+
+        >>> list(adjacent(lambda x: x == 3, range(6), distance=2))
+        [(False, 0), (True, 1), (True, 2), (True, 3), (True, 4), (True, 5)]
+
+    This is useful for contextualizing the results of a search function.
+    For example, a code comparison tool might want to identify lines that
+    have changed, but also surrounding lines to give the viewer of the diff
+    context.
+
+    The predicate function will only be called once for each item in the
+    iterable.
+
+    See also :func:`groupby_transform`, which can be used with this function
+    to group ranges of items with the same `bool` value.
+
+    """
+    # Allow distance=0 mainly for testing that it reproduces results with map()
+    if distance < 0:
+        raise ValueError('distance must be at least 0')
+
+    i1, i2 = tee(iterable)
+    padding = [False] * distance
+    selected = chain(padding, map(predicate, i1), padding)
+    adjacent_to_selected = map(any, windowed(selected, 2 * distance + 1))
+    return zip(adjacent_to_selected, i2)
+
+
+def groupby_transform(iterable, keyfunc=None, valuefunc=None):
+    """An extension of :func:`itertools.groupby` that transforms the values of
+    *iterable* after grouping them.
+    *keyfunc* is a function used to compute a grouping key for each item.
+    *valuefunc* is a function for transforming the items after grouping.
+
+        >>> iterable = 'AaaABbBCcA'
+        >>> keyfunc = lambda x: x.upper()
+        >>> valuefunc = lambda x: x.lower()
+        >>> grouper = groupby_transform(iterable, keyfunc, valuefunc)
+        >>> [(k, ''.join(g)) for k, g in grouper]
+        [('A', 'aaaa'), ('B', 'bbb'), ('C', 'cc'), ('A', 'a')]
+
+    *keyfunc* and *valuefunc* default to identity functions if they are not
+    specified.
+
+    :func:`groupby_transform` is useful when grouping elements of an iterable
+    using a separate iterable as the key. To do this, :func:`zip` the iterables
+    and pass a *keyfunc* that extracts the first element and a *valuefunc*
+    that extracts the second element::
+
+        >>> from operator import itemgetter
+        >>> keys = [0, 0, 1, 1, 1, 2, 2, 2, 3]
+        >>> values = 'abcdefghi'
+        >>> iterable = zip(keys, values)
+        >>> grouper = groupby_transform(iterable, itemgetter(0), itemgetter(1))
+        >>> [(k, ''.join(g)) for k, g in grouper]
+        [(0, 'ab'), (1, 'cde'), (2, 'fgh'), (3, 'i')]
+
+    Note that the order of items in the iterable is significant.
+    Only adjacent items are grouped together, so if you don't want any
+    duplicate groups, you should sort the iterable by the key function.
+
+    """
+    valuefunc = (lambda x: x) if valuefunc is None else valuefunc
+    return ((k, map(valuefunc, g)) for k, g in groupby(iterable, keyfunc))
+
+
+def numeric_range(*args):
+    """An extension of the built-in ``range()`` function whose arguments can
+    be any orderable numeric type.
+
+    With only *stop* specified, *start* defaults to ``0`` and *step*
+    defaults to ``1``. The output items will match the type of *stop*:
+
+        >>> list(numeric_range(3.5))
+        [0.0, 1.0, 2.0, 3.0]
+
+    With only *start* and *stop* specified, *step* defaults to ``1``. The
+    output items will match the type of *start*:
+
+        >>> from decimal import Decimal
+        >>> start = Decimal('2.1')
+        >>> stop = Decimal('5.1')
+        >>> list(numeric_range(start, stop))
+        [Decimal('2.1'), Decimal('3.1'), Decimal('4.1')]
+
+    With *start*, *stop*, and *step*  specified the output items will match
+    the type of ``start + step``:
+
+        >>> from fractions import Fraction
+        >>> start = Fraction(1, 2)  # Start at 1/2
+        >>> stop = Fraction(5, 2)  # End at 5/2
+        >>> step = Fraction(1, 2)  # Count by 1/2
+        >>> list(numeric_range(start, stop, step))
+        [Fraction(1, 2), Fraction(1, 1), Fraction(3, 2), Fraction(2, 1)]
+
+    If *step* is zero, ``ValueError`` is raised. Negative steps are supported:
+
+        >>> list(numeric_range(3, -1, -1.0))
+        [3.0, 2.0, 1.0, 0.0]
+
+    Be aware of the limitations of floating point numbers; the representation
+    of the yielded numbers may be surprising.
+
+    """
+    argc = len(args)
+    if argc == 1:
+        stop, = args
+        start = type(stop)(0)
+        step = 1
+    elif argc == 2:
+        start, stop = args
+        step = 1
+    elif argc == 3:
+        start, stop, step = args
+    else:
+        err_msg = 'numeric_range takes at most 3 arguments, got {}'
+        raise TypeError(err_msg.format(argc))
+
+    values = (start + (step * n) for n in count())
+    if step > 0:
+        return takewhile(partial(gt, stop), values)
+    elif step < 0:
+        return takewhile(partial(lt, stop), values)
+    else:
+        raise ValueError('numeric_range arg 3 must not be zero')
+
+
+def count_cycle(iterable, n=None):
+    """Cycle through the items from *iterable* up to *n* times, yielding
+    the number of completed cycles along with each item. If *n* is omitted the
+    process repeats indefinitely.
+
+    >>> list(count_cycle('AB', 3))
+    [(0, 'A'), (0, 'B'), (1, 'A'), (1, 'B'), (2, 'A'), (2, 'B')]
+
+    """
+    iterable = tuple(iterable)
+    if not iterable:
+        return iter(())
+    counter = count() if n is None else range(n)
+    return ((i, item) for i in counter for item in iterable)
+
+
+def locate(iterable, pred=bool, window_size=None):
+    """Yield the index of each item in *iterable* for which *pred* returns
+    ``True``.
+
+    *pred* defaults to :func:`bool`, which will select truthy items:
+
+        >>> list(locate([0, 1, 1, 0, 1, 0, 0]))
+        [1, 2, 4]
+
+    Set *pred* to a custom function to, e.g., find the indexes for a particular
+    item.
+
+        >>> list(locate(['a', 'b', 'c', 'b'], lambda x: x == 'b'))
+        [1, 3]
+
+    If *window_size* is given, then the *pred* function will be called with
+    that many items. This enables searching for sub-sequences:
+
+        >>> iterable = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
+        >>> pred = lambda *args: args == (1, 2, 3)
+        >>> list(locate(iterable, pred=pred, window_size=3))
+        [1, 5, 9]
+
+    Use with :func:`seekable` to find indexes and then retrieve the associated
+    items:
+
+        >>> from itertools import count
+        >>> from more_itertools import seekable
+        >>> source = (3 * n + 1 if (n % 2) else n // 2 for n in count())
+        >>> it = seekable(source)
+        >>> pred = lambda x: x > 100
+        >>> indexes = locate(it, pred=pred)
+        >>> i = next(indexes)
+        >>> it.seek(i)
+        >>> next(it)
+        106
+
+    """
+    if window_size is None:
+        return compress(count(), map(pred, iterable))
+
+    if window_size < 1:
+        raise ValueError('window size must be at least 1')
+
+    it = windowed(iterable, window_size, fillvalue=_marker)
+    return compress(count(), starmap(pred, it))
+
+
+def lstrip(iterable, pred):
+    """Yield the items from *iterable*, but strip any from the beginning
+    for which *pred* returns ``True``.
+
+    For example, to remove a set of items from the start of an iterable:
+
+        >>> iterable = (None, False, None, 1, 2, None, 3, False, None)
+        >>> pred = lambda x: x in {None, False, ''}
+        >>> list(lstrip(iterable, pred))
+        [1, 2, None, 3, False, None]
+
+    This function is analogous to to :func:`str.lstrip`, and is essentially
+    an wrapper for :func:`itertools.dropwhile`.
+
+    """
+    return dropwhile(pred, iterable)
+
+
+def rstrip(iterable, pred):
+    """Yield the items from *iterable*, but strip any from the end
+    for which *pred* returns ``True``.
+
+    For example, to remove a set of items from the end of an iterable:
+
+        >>> iterable = (None, False, None, 1, 2, None, 3, False, None)
+        >>> pred = lambda x: x in {None, False, ''}
+        >>> list(rstrip(iterable, pred))
+        [None, False, None, 1, 2, None, 3]
+
+    This function is analogous to :func:`str.rstrip`.
+
+    """
+    cache = []
+    cache_append = cache.append
+    for x in iterable:
+        if pred(x):
+            cache_append(x)
+        else:
+            for y in cache:
+                yield y
+            del cache[:]
+            yield x
+
+
+def strip(iterable, pred):
+    """Yield the items from *iterable*, but strip any from the
+    beginning and end for which *pred* returns ``True``.
+
+    For example, to remove a set of items from both ends of an iterable:
+
+        >>> iterable = (None, False, None, 1, 2, None, 3, False, None)
+        >>> pred = lambda x: x in {None, False, ''}
+        >>> list(strip(iterable, pred))
+        [1, 2, None, 3]
+
+    This function is analogous to :func:`str.strip`.
+
+    """
+    return rstrip(lstrip(iterable, pred), pred)
+
+
+def islice_extended(iterable, *args):
+    """An extension of :func:`itertools.islice` that supports negative values
+    for *stop*, *start*, and *step*.
+
+        >>> iterable = iter('abcdefgh')
+        >>> list(islice_extended(iterable, -4, -1))
+        ['e', 'f', 'g']
+
+    Slices with negative values require some caching of *iterable*, but this
+    function takes care to minimize the amount of memory required.
+
+    For example, you can use a negative step with an infinite iterator:
+
+        >>> from itertools import count
+        >>> list(islice_extended(count(), 110, 99, -2))
+        [110, 108, 106, 104, 102, 100]
+
+    """
+    s = slice(*args)
+    start = s.start
+    stop = s.stop
+    if s.step == 0:
+        raise ValueError('step argument must be a non-zero integer or None.')
+    step = s.step or 1
+
+    it = iter(iterable)
+
+    if step > 0:
+        start = 0 if (start is None) else start
+
+        if (start < 0):
+            # Consume all but the last -start items
+            cache = deque(enumerate(it, 1), maxlen=-start)
+            len_iter = cache[-1][0] if cache else 0
+
+            # Adjust start to be positive
+            i = max(len_iter + start, 0)
+
+            # Adjust stop to be positive
+            if stop is None:
+                j = len_iter
+            elif stop >= 0:
+                j = min(stop, len_iter)
+            else:
+                j = max(len_iter + stop, 0)
+
+            # Slice the cache
+            n = j - i
+            if n <= 0:
+                return
+
+            for index, item in islice(cache, 0, n, step):
+                yield item
+        elif (stop is not None) and (stop < 0):
+            # Advance to the start position
+            next(islice(it, start, start), None)
+
+            # When stop is negative, we have to carry -stop items while
+            # iterating
+            cache = deque(islice(it, -stop), maxlen=-stop)
+
+            for index, item in enumerate(it):
+                cached_item = cache.popleft()
+                if index % step == 0:
+                    yield cached_item
+                cache.append(item)
+        else:
+            # When both start and stop are positive we have the normal case
+            for item in islice(it, start, stop, step):
+                yield item
+    else:
+        start = -1 if (start is None) else start
+
+        if (stop is not None) and (stop < 0):
+            # Consume all but the last items
+            n = -stop - 1
+            cache = deque(enumerate(it, 1), maxlen=n)
+            len_iter = cache[-1][0] if cache else 0
+
+            # If start and stop are both negative they are comparable and
+            # we can just slice. Otherwise we can adjust start to be negative
+            # and then slice.
+            if start < 0:
+                i, j = start, stop
+            else:
+                i, j = min(start - len_iter, -1), None
+
+            for index, item in list(cache)[i:j:step]:
+                yield item
+        else:
+            # Advance to the stop position
+            if stop is not None:
+                m = stop + 1
+                next(islice(it, m, m), None)
+
+            # stop is positive, so if start is negative they are not comparable
+            # and we need the rest of the items.
+            if start < 0:
+                i = start
+                n = None
+            # stop is None and start is positive, so we just need items up to
+            # the start index.
+            elif stop is None:
+                i = None
+                n = start + 1
+            # Both stop and start are positive, so they are comparable.
+            else:
+                i = None
+                n = start - stop
+                if n <= 0:
+                    return
+
+            cache = list(islice(it, n))
+
+            for item in cache[i::step]:
+                yield item
+
+
+def always_reversible(iterable):
+    """An extension of :func:`reversed` that supports all iterables, not
+    just those which implement the ``Reversible`` or ``Sequence`` protocols.
+
+        >>> print(*always_reversible(x for x in range(3)))
+        2 1 0
+
+    If the iterable is already reversible, this function returns the
+    result of :func:`reversed()`. If the iterable is not reversible,
+    this function will cache the remaining items in the iterable and
+    yield them in reverse order, which may require significant storage.
+    """
+    try:
+        return reversed(iterable)
+    except TypeError:
+        return reversed(list(iterable))
+
+
+def consecutive_groups(iterable, ordering=lambda x: x):
+    """Yield groups of consecutive items using :func:`itertools.groupby`.
+    The *ordering* function determines whether two items are adjacent by
+    returning their position.
+
+    By default, the ordering function is the identity function. This is
+    suitable for finding runs of numbers:
+
+        >>> iterable = [1, 10, 11, 12, 20, 30, 31, 32, 33, 40]
+        >>> for group in consecutive_groups(iterable):
+        ...     print(list(group))
+        [1]
+        [10, 11, 12]
+        [20]
+        [30, 31, 32, 33]
+        [40]
+
+    For finding runs of adjacent letters, try using the :meth:`index` method
+    of a string of letters:
+
+        >>> from string import ascii_lowercase
+        >>> iterable = 'abcdfgilmnop'
+        >>> ordering = ascii_lowercase.index
+        >>> for group in consecutive_groups(iterable, ordering):
+        ...     print(list(group))
+        ['a', 'b', 'c', 'd']
+        ['f', 'g']
+        ['i']
+        ['l', 'm', 'n', 'o', 'p']
+
+    """
+    for k, g in groupby(
+        enumerate(iterable), key=lambda x: x[0] - ordering(x[1])
+    ):
+        yield map(itemgetter(1), g)
+
+
+def difference(iterable, func=sub):
+    """By default, compute the first difference of *iterable* using
+    :func:`operator.sub`.
+
+        >>> iterable = [0, 1, 3, 6, 10]
+        >>> list(difference(iterable))
+        [0, 1, 2, 3, 4]
+
+    This is the opposite of :func:`accumulate`'s default behavior:
+
+        >>> from more_itertools import accumulate
+        >>> iterable = [0, 1, 2, 3, 4]
+        >>> list(accumulate(iterable))
+        [0, 1, 3, 6, 10]
+        >>> list(difference(accumulate(iterable)))
+        [0, 1, 2, 3, 4]
+
+    By default *func* is :func:`operator.sub`, but other functions can be
+    specified. They will be applied as follows::
+
+        A, B, C, D, ... --> A, func(B, A), func(C, B), func(D, C), ...
+
+    For example, to do progressive division:
+
+        >>> iterable = [1, 2, 6, 24, 120]  # Factorial sequence
+        >>> func = lambda x, y: x // y
+        >>> list(difference(iterable, func))
+        [1, 2, 3, 4, 5]
+
+    """
+    a, b = tee(iterable)
+    try:
+        item = next(b)
+    except StopIteration:
+        return iter([])
+    return chain([item], map(lambda x: func(x[1], x[0]), zip(a, b)))
+
+
+class SequenceView(Sequence):
+    """Return a read-only view of the sequence object *target*.
+
+    :class:`SequenceView` objects are analogous to Python's built-in
+    "dictionary view" types. They provide a dynamic view of a sequence's items,
+    meaning that when the sequence updates, so does the view.
+
+        >>> seq = ['0', '1', '2']
+        >>> view = SequenceView(seq)
+        >>> view
+        SequenceView(['0', '1', '2'])
+        >>> seq.append('3')
+        >>> view
+        SequenceView(['0', '1', '2', '3'])
+
+    Sequence views support indexing, slicing, and length queries. They act
+    like the underlying sequence, except they don't allow assignment:
+
+        >>> view[1]
+        '1'
+        >>> view[1:-1]
+        ['1', '2']
+        >>> len(view)
+        4
+
+    Sequence views are useful as an alternative to copying, as they don't
+    require (much) extra storage.
+
+    """
+    def __init__(self, target):
+        if not isinstance(target, Sequence):
+            raise TypeError
+        self._target = target
+
+    def __getitem__(self, index):
+        return self._target[index]
+
+    def __len__(self):
+        return len(self._target)
+
+    def __repr__(self):
+        return '{}({})'.format(self.__class__.__name__, repr(self._target))
+
+
+class seekable(object):
+    """Wrap an iterator to allow for seeking backward and forward. This
+    progressively caches the items in the source iterable so they can be
+    re-visited.
+
+    Call :meth:`seek` with an index to seek to that position in the source
+    iterable.
+
+    To "reset" an iterator, seek to ``0``:
+
+        >>> from itertools import count
+        >>> it = seekable((str(n) for n in count()))
+        >>> next(it), next(it), next(it)
+        ('0', '1', '2')
+        >>> it.seek(0)
+        >>> next(it), next(it), next(it)
+        ('0', '1', '2')
+        >>> next(it)
+        '3'
+
+    You can also seek forward:
+
+        >>> it = seekable((str(n) for n in range(20)))
+        >>> it.seek(10)
+        >>> next(it)
+        '10'
+        >>> 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), next(it), next(it)
+        ('0', '1', '2')
+
+    The cache grows as the source iterable progresses, so beware of wrapping
+    very large or infinite iterables.
+
+    You may view the contents of the cache with the :meth:`elements` method.
+    That returns a :class:`SequenceView`, a view that updates automatically:
+
+        >>> it = seekable((str(n) for n in range(10)))
+        >>> next(it), next(it), next(it)
+        ('0', '1', '2')
+        >>> elements = it.elements()
+        >>> elements
+        SequenceView(['0', '1', '2'])
+        >>> next(it)
+        '3'
+        >>> elements
+        SequenceView(['0', '1', '2', '3'])
+
+    """
+
+    def __init__(self, iterable):
+        self._source = iter(iterable)
+        self._cache = []
+        self._index = None
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self._index is not None:
+            try:
+                item = self._cache[self._index]
+            except IndexError:
+                self._index = None
+            else:
+                self._index += 1
+                return item
+
+        item = next(self._source)
+        self._cache.append(item)
+        return item
+
+    next = __next__
+
+    def elements(self):
+        return SequenceView(self._cache)
+
+    def seek(self, index):
+        self._index = index
+        remainder = index - len(self._cache)
+        if remainder > 0:
+            consume(self, remainder)
+
+
+class run_length(object):
+    """
+    :func:`run_length.encode` compresses an iterable with run-length encoding.
+    It yields groups of repeated items with the count of how many times they
+    were repeated:
+
+        >>> uncompressed = 'abbcccdddd'
+        >>> list(run_length.encode(uncompressed))
+        [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
+
+    :func:`run_length.decode` decompresses an iterable that was previously
+    compressed with run-length encoding. It yields the items of the
+    decompressed iterable:
+
+        >>> compressed = [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
+        >>> list(run_length.decode(compressed))
+        ['a', 'b', 'b', 'c', 'c', 'c', 'd', 'd', 'd', 'd']
+
+    """
+
+    @staticmethod
+    def encode(iterable):
+        return ((k, ilen(g)) for k, g in groupby(iterable))
+
+    @staticmethod
+    def decode(iterable):
+        return chain.from_iterable(repeat(k, n) for k, n in iterable)
+
+
+def exactly_n(iterable, n, predicate=bool):
+    """Return ``True`` if exactly ``n`` items in the iterable are ``True``
+    according to the *predicate* function.
+
+        >>> exactly_n([True, True, False], 2)
+        True
+        >>> exactly_n([True, True, False], 1)
+        False
+        >>> exactly_n([0, 1, 2, 3, 4, 5], 3, lambda x: x < 3)
+        True
+
+    The iterable will be advanced until ``n + 1`` truthy items are encountered,
+    so avoid calling it on infinite iterables.
+
+    """
+    return len(take(n + 1, filter(predicate, iterable))) == n
+
+
+def circular_shifts(iterable):
+    """Return a list of circular shifts of *iterable*.
+
+        >>> circular_shifts(range(4))
+        [(0, 1, 2, 3), (1, 2, 3, 0), (2, 3, 0, 1), (3, 0, 1, 2)]
+    """
+    lst = list(iterable)
+    return take(len(lst), windowed(cycle(lst), len(lst)))
+
+
+def make_decorator(wrapping_func, result_index=0):
+    """Return a decorator version of *wrapping_func*, which is a function that
+    modifies an iterable. *result_index* is the position in that function's
+    signature where the iterable goes.
+
+    This lets you use itertools on the "production end," i.e. at function
+    definition. This can augment what the function returns without changing the
+    function's code.
+
+    For example, to produce a decorator version of :func:`chunked`:
+
+        >>> from more_itertools import chunked
+        >>> chunker = make_decorator(chunked, result_index=0)
+        >>> @chunker(3)
+        ... def iter_range(n):
+        ...     return iter(range(n))
+        ...
+        >>> list(iter_range(9))
+        [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+
+    To only allow truthy items to be returned:
+
+        >>> truth_serum = make_decorator(filter, result_index=1)
+        >>> @truth_serum(bool)
+        ... def boolean_test():
+        ...     return [0, 1, '', ' ', False, True]
+        ...
+        >>> list(boolean_test())
+        [1, ' ', True]
+
+    The :func:`peekable` and :func:`seekable` wrappers make for practical
+    decorators:
+
+        >>> from more_itertools import peekable
+        >>> peekable_function = make_decorator(peekable)
+        >>> @peekable_function()
+        ... def str_range(*args):
+        ...     return (str(x) for x in range(*args))
+        ...
+        >>> it = str_range(1, 20, 2)
+        >>> next(it), next(it), next(it)
+        ('1', '3', '5')
+        >>> it.peek()
+        '7'
+        >>> next(it)
+        '7'
+
+    """
+    # See https://sites.google.com/site/bbayles/index/decorator_factory for
+    # notes on how this works.
+    def decorator(*wrapping_args, **wrapping_kwargs):
+        def outer_wrapper(f):
+            def inner_wrapper(*args, **kwargs):
+                result = f(*args, **kwargs)
+                wrapping_args_ = list(wrapping_args)
+                wrapping_args_.insert(result_index, result)
+                return wrapping_func(*wrapping_args_, **wrapping_kwargs)
+
+            return inner_wrapper
+
+        return outer_wrapper
+
+    return decorator
+
+
+def map_reduce(iterable, keyfunc, valuefunc=None, reducefunc=None):
+    """Return a dictionary that maps the items in *iterable* to categories
+    defined by *keyfunc*, transforms them with *valuefunc*, and
+    then summarizes them by category with *reducefunc*.
+
+    *valuefunc* defaults to the identity function if it is unspecified.
+    If *reducefunc* is unspecified, no summarization takes place:
+
+        >>> keyfunc = lambda x: x.upper()
+        >>> result = map_reduce('abbccc', keyfunc)
+        >>> sorted(result.items())
+        [('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])]
+
+    Specifying *valuefunc* transforms the categorized items:
+
+        >>> keyfunc = lambda x: x.upper()
+        >>> valuefunc = lambda x: 1
+        >>> result = map_reduce('abbccc', keyfunc, valuefunc)
+        >>> sorted(result.items())
+        [('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])]
+
+    Specifying *reducefunc* summarizes the categorized items:
+
+        >>> keyfunc = lambda x: x.upper()
+        >>> valuefunc = lambda x: 1
+        >>> reducefunc = sum
+        >>> result = map_reduce('abbccc', keyfunc, valuefunc, reducefunc)
+        >>> sorted(result.items())
+        [('A', 1), ('B', 2), ('C', 3)]
+
+    You may want to filter the input iterable before applying the map/reduce
+    procedure:
+
+        >>> all_items = range(30)
+        >>> items = [x for x in all_items if 10 <= x <= 20]  # Filter
+        >>> keyfunc = lambda x: x % 2  # Evens map to 0; odds to 1
+        >>> categories = map_reduce(items, keyfunc=keyfunc)
+        >>> sorted(categories.items())
+        [(0, [10, 12, 14, 16, 18, 20]), (1, [11, 13, 15, 17, 19])]
+        >>> summaries = map_reduce(items, keyfunc=keyfunc, reducefunc=sum)
+        >>> sorted(summaries.items())
+        [(0, 90), (1, 75)]
+
+    Note that all items in the iterable are gathered into a list before the
+    summarization step, which may require significant storage.
+
+    The returned object is a :obj:`collections.defaultdict` with the
+    ``default_factory`` set to ``None``, such that it behaves like a normal
+    dictionary.
+
+    """
+    valuefunc = (lambda x: x) if (valuefunc is None) else valuefunc
+
+    ret = defaultdict(list)
+    for item in iterable:
+        key = keyfunc(item)
+        value = valuefunc(item)
+        ret[key].append(value)
+
+    if reducefunc is not None:
+        for key, value_list in ret.items():
+            ret[key] = reducefunc(value_list)
+
+    ret.default_factory = None
+    return ret
+
+
+def rlocate(iterable, pred=bool, window_size=None):
+    """Yield the index of each item in *iterable* for which *pred* returns
+    ``True``, starting from the right and moving left.
+
+    *pred* defaults to :func:`bool`, which will select truthy items:
+
+        >>> list(rlocate([0, 1, 1, 0, 1, 0, 0]))  # Truthy at 1, 2, and 4
+        [4, 2, 1]
+
+    Set *pred* to a custom function to, e.g., find the indexes for a particular
+    item:
+
+        >>> iterable = iter('abcb')
+        >>> pred = lambda x: x == 'b'
+        >>> list(rlocate(iterable, pred))
+        [3, 1]
+
+    If *window_size* is given, then the *pred* function will be called with
+    that many items. This enables searching for sub-sequences:
+
+        >>> iterable = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
+        >>> pred = lambda *args: args == (1, 2, 3)
+        >>> list(rlocate(iterable, pred=pred, window_size=3))
+        [9, 5, 1]
+
+    Beware, this function won't return anything for infinite iterables.
+    If *iterable* is reversible, ``rlocate`` will reverse it and search from
+    the right. Otherwise, it will search from the left and return the results
+    in reverse order.
+
+    See :func:`locate` to for other example applications.
+
+    """
+    if window_size is None:
+        try:
+            len_iter = len(iterable)
+            return (
+                len_iter - i - 1 for i in locate(reversed(iterable), pred)
+            )
+        except TypeError:
+            pass
+
+    return reversed(list(locate(iterable, pred, window_size)))
+
+
+def replace(iterable, pred, substitutes, count=None, window_size=1):
+    """Yield the items from *iterable*, replacing the items for which *pred*
+    returns ``True`` with the items from the iterable *substitutes*.
+
+        >>> iterable = [1, 1, 0, 1, 1, 0, 1, 1]
+        >>> pred = lambda x: x == 0
+        >>> substitutes = (2, 3)
+        >>> list(replace(iterable, pred, substitutes))
+        [1, 1, 2, 3, 1, 1, 2, 3, 1, 1]
+
+    If *count* is given, the number of replacements will be limited:
+
+        >>> iterable = [1, 1, 0, 1, 1, 0, 1, 1, 0]
+        >>> pred = lambda x: x == 0
+        >>> substitutes = [None]
+        >>> list(replace(iterable, pred, substitutes, count=2))
+        [1, 1, None, 1, 1, None, 1, 1, 0]
+
+    Use *window_size* to control the number of items passed as arguments to
+    *pred*. This allows for locating and replacing subsequences.
+
+        >>> iterable = [0, 1, 2, 5, 0, 1, 2, 5]
+        >>> window_size = 3
+        >>> pred = lambda *args: args == (0, 1, 2)  # 3 items passed to pred
+        >>> substitutes = [3, 4] # Splice in these items
+        >>> list(replace(iterable, pred, substitutes, window_size=window_size))
+        [3, 4, 5, 3, 4, 5]
+
+    """
+    if window_size < 1:
+        raise ValueError('window_size must be at least 1')
+
+    # Save the substitutes iterable, since it's used more than once
+    substitutes = tuple(substitutes)
+
+    # Add padding such that the number of windows matches the length of the
+    # iterable
+    it = chain(iterable, [_marker] * (window_size - 1))
+    windows = windowed(it, window_size)
+
+    n = 0
+    for w in windows:
+        # If the current window matches our predicate (and we haven't hit
+        # our maximum number of replacements), splice in the substitutes
+        # and then consume the following windows that overlap with this one.
+        # For example, if the iterable is (0, 1, 2, 3, 4...)
+        # and the window size is 2, we have (0, 1), (1, 2), (2, 3)...
+        # If the predicate matches on (0, 1), we need to zap (0, 1) and (1, 2)
+        if pred(*w):
+            if (count is None) or (n < count):
+                n += 1
+                for s in substitutes:
+                    yield s
+                consume(windows, window_size - 1)
+                continue
+
+        # If there was no match (or we've reached the replacement limit),
+        # yield the first item from the window.
+        if w and (w[0] is not _marker):
+            yield w[0]